AS5.10 | Emerging techniques for diverse air quality applications in data scarce low- and middle-income countries
EDI
Emerging techniques for diverse air quality applications in data scarce low- and middle-income countries
Convener: Shahzad GaniECSECS | Co-conveners: Rebecca Garland, Sarath Guttikunda, Aderiana MbandiECSECS, Nestor Rojas
Orals
| Mon, 15 Apr, 08:30–12:25 (CEST), 14:00–15:45 (CEST)
 
Room M1
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
 
Hall X5
Posters virtual
| Attendance Tue, 16 Apr, 14:00–15:45 (CEST) | Display Tue, 16 Apr, 08:30–18:00
 
vHall X5
Orals |
Mon, 08:30
Tue, 10:45
Tue, 14:00
Air pollution is a leading environmental risk factor for people living in low- and middle-income countries (LMICs). These regions have diverse sources of air pollution (e.g., industrial, domestic burning, biomass burning, traffic) and atmospheric processes that influence the pollution loadings (e.g., boundary layer dynamics, long-range transport, secondary air pollution). Air quality management in data scarce regions with high temporal and spatial diversity in air pollution sources and atmospheric processes poses complex challenges. While the air quality may vary greatly within and between countries in data scarce LMICs, the need for evidence-based approaches for improving air quality with limited data are common.

This session will bring together participants working on measurement- and/or modeling-based approaches for air quality applications in Africa, Latin America, South Asia, and Southeast Asia. These applications can range from emission inventories, chemical transport modeling, chemical analysis, source apportionment, regulatory and hybrid monitoring, air quality forecasting, scenario analysis, health impacts, and other policy applications at urban, rural, national, and regional scales. We expect the participants to engage meaningfully and critically with at least one practical application of their analysis in the context of science, technology, policy, citizen engagement, capacity building, or institutional development. We also expect the participants to highlight the interlinkages of their work in a broader context within and between regions in data scarce LMICs as they relate to aspects such as knowledge exchange/sharing, data sharing, data management, analytical techniques, or other evidence-based approaches to air quality management.

Orals: Mon, 15 Apr | Room M1

Chairpersons: Shahzad Gani, Rebecca Garland, Nestor Rojas
08:30–08:35
08:35–08:45
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EGU24-1094
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ECS
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On-site presentation
Lucas Berná, Ana Isabel López-Noreña, Rafael Pedro Fernandez, and S. Enrique Puliafito

A Partial Least Squares Path Modeling (PLS-PM) methodology was employed to identify the dominant emission sectors responsible for the concentrations of major air pollutants. This approach leverages factor analysis and principal component analysis to differentiate pollutant concentration levels into clusters and establish causal relationships with specific emission sectors. As categorical variables (measurable data) for this model, seven WRF-Chem simulations were conducted over a single domain encompassing Argentina for April 2019. These simulations utilized the GEAA-AEI emission inventory, following a sectorized approach. Five simulations incorporated emissions from individual sectors (Energy, Transport, Livestock, Residential, Industrial), one simulation included emissions from all sectors, and a control simulation was conducted with anthropogenic emissions deactivated. The PLS-PM analysis facilitated the creation of a color map figure that distinguishes areas impacted by each sector. This distinction is particularly evident for transport and livestock emissions at low-pollution levels, while hotspots related to energy sector emissions are discernible in high-pollution areas. This modeling approach holds promise for extracting additional information about areas with high pollution levels from future WRF-Chem simulations or observational data, thereby enabling the identification of contributing sources.

How to cite: Berná, L., López-Noreña, A. I., Fernandez, R. P., and Puliafito, S. E.: Identifying Dominant Emission Sectors for Air Quality in Argentina using Partial Least Squares Path Modeling (PLS-PM) on WRF-Chem Simulations., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1094, https://doi.org/10.5194/egusphere-egu24-1094, 2024.

08:45–08:55
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EGU24-1167
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ECS
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On-site presentation
Sebastian Diez, Josefina Urquiza, Tailine Correa, and Colleen Rosales

Air pollution has a wide range of harmful effects on public health, ranging from respiratory and cardiovascular problems to metabolic and neurological disorders. As such, characterizing air pollutants is of utmost importance, particularly in regions where environmental injustice is a widespread problem. This study focuses on Latin America, a region where many countries face increased vulnerability due to factors such as limited access to healthcare and inadequate availability of air quality information and medical records. Using an integrative methodology, we combined data from the latest reported data (2022-2023) from reference monitors and air sensors reported in OpenAQ metrics from the Global Burden of Disease study (i.e., years of life lost due to premature mortality or YLLs; years lived with disability or YLDs; and disability‐adjusted life years or DALYs) to analyze correlations and trends. Our initial analysis reveals that countries without air quality monitoring (41% of countries in the region) exhibit an average mortality rate approximately 20% higher than countries with monitoring in place and approximately 35% higher than countries with completely open data. This disparity in monitoring is not just a matter of data availability but reflects deeper socio-economic challenges. Specifically, we found that the burden of disease is significantly higher in countries with lower development, highlighting a major socio-economic dimension in understanding and addressing the health impacts of air pollution. Countries with a low Social Development Index (SDI) showed more than a 5-fold increase in Disability-Adjusted Life Years (DALY) rates for ischemic heart disease and stroke compared to high SDI countries. Furthermore, countries in the lowest economic bracket had a nearly 7-fold higher DALY rate for stroke when compared to very higher-income countries. These findings underscore the deep interconnection between a country's socioeconomic level and the health risks associated with air pollution. However, it is essential to note that these findings do not imply causality, but rather offer a snapshot of the current situation. Additionally, factors such as public health policies, economic development, and socio-environmental conditions must be considered to fully understand these differences. To develop strategies that positively impact the general health of the region, it is essential to take other relevant factors into consideration. This study could serve as a basis for more in-depth research and for the formulation of more informed and effective policies in the region. However, it is essential to note that these findings do not imply causality but rather offer a snapshot of the current situation. Additionally, factors such as public health policies, economic development, and socio-environmental conditions must be considered to fully understand these differences. To develop strategies that positively impact the general health of the region, it is essential to take other relevant factors into consideration. This study could serve as a basis for more in-depth research and for the formulation of more informed and effective policies in the region.

How to cite: Diez, S., Urquiza, J., Correa, T., and Rosales, C.: Unveiling the hidden: air pollution monitoring and health outcomes in LatAm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1167, https://doi.org/10.5194/egusphere-egu24-1167, 2024.

08:55–09:05
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EGU24-10283
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ECS
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On-site presentation
Ricky Nathvani, Sierra Clark, Absosede Sarah Alli, Vishwanath Doreswamy-Gowda, Jiayuan Wang, Michael Brauer, James Nimo, Josephine Bedford Moses, Solomon Baah, Alison Hughes, Samuel Agyei-Mensah, James Bennett, Raphael E Arku, and Majid Ezzati

Air and noise pollution are significant emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a major barrier to the formulation and evaluation of policies to reduce air and noise pollution.

We designed and carried out an innovative 3-year measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design used a combination of fixed (3 year-long, n=10) and rotating (week-long over 1 year, n =136) sites, selected to represent a range of land uses and source influences. We collected data on PM2.5, black carbon (BC), nitrogen oxides (NOx), weather variables, noise pollution, along with street level time-lapse images with cameras. To do this, we strategically deployed low-cost, low-power, lightweight monitoring devices in an integrated station that was robust, socially unobtrusive, and able to function in the West African coastal climate. We used spatiotemporal land use regression models to predict PM2.5, NO2, BC and noise pollution across the city in high spatial resolution, and state-of-the-art methods in deep learning to predict pollution levels in high temporal resolution by training classification algorithms on 2 million time-lapse images captured at street level with corresponding pollution measurements. 

Most measurement sites recorded air pollution and noise levels above the WHO health-based guidelines. Spatiotemporal LUR models achieved good out of sample R2’s of 0.51-0.54 (noise), 0.58 – 0.83 (PM2.5), 0.78 - 0.80 (NO2) and 0.79 – 0.88 (BC). From the deep learning image-based analysis, the classification (prediction) accuracy of noise levels in space and time was higher (40-70%) than PM2.5 (30-55%), due to the localised nature of noise source emissions, and the fine-grained nature of our classes, which distinguish between small changes than previous studies.  

Our approach to monitoring and modelling air and noise pollution can be scaled up in other SSA cities to fill critical data gaps, and is already being successfully piloted in Kigali, Rwanda. The exposure surfaces developed with the LUR models are now supporting ongoing epidemiological studies assessing the impact of exposure to air and noise on birth outcomes and child health and development in Accra. Street view imagery are an increasingly available resource in cities around the world (from CCTV; Google Street View), and results from our deep learning image-based analysis show that the time lapsed images are a uniquely informative source of data for predicting high resolution temporal change in exposure, simultaneously with the presence or absence of potential determinants, though integrating with high spatial resolution remains a challenge.

How to cite: Nathvani, R., Clark, S., Alli, A. S., Doreswamy-Gowda, V., Wang, J., Brauer, M., Nimo, J., Moses, J. B., Baah, S., Hughes, A., Agyei-Mensah, S., Bennett, J., Arku, R. E., and Ezzati, M.: High-resolution spatiotemporal measurement and city-scale modelling of air and noise pollution in Accra Ghana: sensors, deep learning, and street-view imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10283, https://doi.org/10.5194/egusphere-egu24-10283, 2024.

09:05–09:15
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EGU24-1057
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ECS
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On-site presentation
Valeria Solórzano-Araque, Sebastián Carmona-Estrada, Pablo A Osorio, Santiago Isaza-Cadavid, Lisseth Cruz-Ruiz, Olga Lucía Quintero, Nicolás Pinel, and Santiago Lopez-Restrepo

The study presents a framework for a human exposure model to pollution based on Chemical Transport Models (CTMs) for the urban area of Medellín in Colombia, including the metropolitan area in which is situated in the the city. The exposure model utilises pollutant concentration simulations obtained from the LOTOS-EUROS CTM drived by the WRF Numerical Weather Model (NWM) simulations conducted in January 2019 within the Aburrá Valley. The simulations provide spatial and temporal resolutions of 1 km x 1 km and 1 hour, correspondingly. This study applies the data from the 2017 Origin Destination Survey, which surveyed 16,340 households and 36,364 individuals living in urban and rural areas of the 10 municipalities of the Aburrá Valley. This data on demographic variables and weekday travel patterns helped to analyse human behaviour and intra-urban migration in order to develop a precise measure of daily pollution exposure for different geographic zones. The total mean exposure is calculated from the linear combination of  the exposure time and the mean concentration in every zone.

The study examined the morphological composition of fine particulate matter PM2.5 and PM10, resulting in the identification of six distinct groups, including mineral, organic/biogenic, tire wear, metallic, paint with high titanium content, and salts. Cytogenotoxicity tests assess the survival rates of cells in eye, skin and lung tissues. The resulting data is extrapolated to determine an irrigation factor specific to the urban area. It is worth highlighting that the central and southern regions of the Aburrá Valley, designated by high population density and multiple sources of pollution, exhibit heightened pollution exposure. On the other hand, the assessment of high-risk regions in the central zone is influenced by the lack of information in other parts of the urban area.

How to cite: Solórzano-Araque, V., Carmona-Estrada, S., Osorio, P. A., Isaza-Cadavid, S., Cruz-Ruiz, L., Quintero, O. L., Pinel, N., and Lopez-Restrepo, S.: Exposure to pollutants Risk model in the Aburrá Valley (Expor2), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1057, https://doi.org/10.5194/egusphere-egu24-1057, 2024.

09:15–09:25
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EGU24-1178
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On-site presentation
Dayana Agudelo Castañeda, Sandra Maldonado, Maria Jose Nieto, Julio Davila, Julian Arellana, and Daniel Oviedo

Examining the distribution of both the generation and exposure to traffic-related air pollution across diverse population groups stands as a pivotal environmental justice issue. From a social equity perspective, numerous inquiries arise when scrutinizing commuting patterns within metropolitan areas. Everyday urban mobility entails repeated and sometimes prolonged exposure to traffic-related air pollutants. PM2.5 and Black Carbon have been identified as one of the major pollutants and established as a health hazard.  As part of an interdisciplinary collaboration of experts on urban policy, air quality and transport studies the aim of this research is to assess inequity in patterns of personal exposure to PM2.5 and BC among users of different modes of transportation, including informal public transport. The case of study was Soledad, Colombia. Utilizing household surveys, we crafted user profiles for various modes of transportation, drawing insights from comprehensive data gathered through these surveys. Based on the analysis of mobility patterns, a cross-sectional study was designed to evaluate personal exposure to PM2.5 and BC during trips within the city. Pollutants were measured in real time, during peak, off-peak and weekend hours along typical routes defined by motorized three-wheelers drivers, which were taken as predefined routes for the other modes (car, bus). The average daily inhalation dose within the microenvironment was calculated differentiated by gender and age, according to the daily exposure factor. The findings unveiled significant disparities in PM2.5 and BC exposure, notably affecting adult women and individuals with disabilities, particularly those who frequently use motorized three-wheelers. Despite their lower exposure factor, these groups exhibited a higher dose of pollutants. This inequality underscores the crucial need to incorporate considerations of both accessibility and air quality in the formulation of sustainable urban transport policies.

Key words: inequity; PM2.5; eBC; low-cost sensors; urban mobility; personal exposure, transport modes, environmental justice.

How to cite: Agudelo Castañeda, D., Maldonado, S., Nieto, M. J., Davila, J., Arellana, J., and Oviedo, D.: Inequity in personal exposure to PM2.5 and BC in transport microenvironments: a study case in Barranquilla metropolitan region, Colombia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1178, https://doi.org/10.5194/egusphere-egu24-1178, 2024.

09:25–09:35
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EGU24-186
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Virtual presentation
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Andrea Pineda Rojas and Emilio Kropff

Urban-scale atmospheric dispersion models play a crucial role in air quality (AQ) management, enabling the evaluation of pollutant concentration distribution in unsampled regions and determining necessary emission reductions for compliance with local regulations. However, a significant challenge in implementing air quality models is that their performance assessment requires observations from numerous AQ monitoring stations, a resource often lacking in low and middle-income countries. This constraint is particularly evident in the case of the DAUMOD-GRS model, developed for estimating nitrogen dioxide (NO2) and ozone (O3) concentrations in the Metropolitan Area of Buenos Aires (MABA), where AQ monitoring is scarce. In an effort to overcome this limitation and comprehensively understand model outcomes, even in non-monitored areas, we have devised two innovative methods employing big data techniques. The first method focuses on analysing both input and output (I/O) conditions that are associated with elevated air pollutant concentrations, without relying on observational data. For instance, applying a clustering analysis to an ensemble of I/O data related to summer maximum O3 concentrations in the MABA showed four distinct solution patterns varying with emissions. This analysis revealed different ozone dynamics in the suburban areas. A similar approach used to investigate conditions leading to elevated hourly NO2 concentrations suggested that the model's memory effect could contribute significantly to overestimations in low emission zones of the MABA under conditions of low wind speed. The second method was used to analyse the first long time series of hourly NO2 concentrations measured in the city, which have become recently available. This has allowed a comprehensive assessment of the performance of DAUMOD-GRS. While the model shows an overall acceptable performance at the three monitoring sites, a complementary methodology was introduced to discern whether errors are randomly distributed or concentrated in specific regions within the space of the input data conditions. Employing a k-means algorithm on three daily-calculated performance metrics (FB, NMSE and R), we ranked days according to their levels of model performance. This approach revealed a systematic underestimation of NO2 concentration at the coastal monitoring site when winds come from the river, suggesting a significant impact of the southernmost power plant. Furthermore, it highlighted that the removal of the memory effect leads to an improved estimate of the daily maximum NO2 concentrations. Subsequent re-evaluation of the first method after this modification identified a large number of NO2 events concentrated in a few hours during warm months. A detailed analysis of these cases revealed a change in the reporting of low wind speed values from 2010 onwards. These examples show that analysing both I/O data of high pollutant concentrations and disaggregating model errors by short time periods can help identify possible model improvements and increase confidence in model results in a context of limited air quality monitoring.

How to cite: Pineda Rojas, A. and Kropff, E.: Big data techniques to improve the performance of an air quality model in a mega-city with limited air pollution monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-186, https://doi.org/10.5194/egusphere-egu24-186, 2024.

09:35–09:45
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EGU24-138
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ECS
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Virtual presentation
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Riaz Hossain Khan, Anisur Rahman Bayazid, Martha Lee, Md. Kamrul Hasan, Tasnim Abdary Anonna, Lauren Rosenthal, Zahidul Quayyum, Benjamin Barratt, and Jill Baumgartner

Existing literature reported high concentrations of ambient particulates, specifically during the dry months, severely affecting street and construction site workers, elderly, and school-aged children in Dhaka city, Bangladesh. The available air quality data from only three continuous air monitoring stations is inadequate for any evidence-based study regarding public health outcomes. Significant knowledge gaps exist due to air monitoring networks being limited to low-cost optical sensors, key suburban areas, slum areas, and industrial areas remaining uncovered and little information about sources contributing to air pollution. Therefore, this ongoing air monitoring study aimed to conduct a city-wide measurement campaign of fine particulate matter (PM2.5) and black carbon (BC), investigate source contributors to air pollution using source apportionment analysis, and produce spatiotemporal land use regression (LUR) models to estimate PM2.5 and BC concentrations across the city. The study team has recently conducted two seasonal (two months each) air monitoring campaigns from systematically designed eight fixed sites and sixty-one rotating sites considering the major land use classes across the city domain. Sites covered different land use types such as commercial, residential, industrial, suburban, major roads, green space, and brick kilns. The significant challenges during the air monitoring campaign included high road traffic from religious congress, political protests, and waterlogging from sudden intense rainfall. Weather conditions such as high heat and heavy rain affected the functioning and performance of equipment. Besides, exposure of the field team to dengue outbreaks, particularly during the wet season, had to be dealt with. Preliminary results showed that the unadjusted concentration of PM2.5 from the Zefan sensors was substantially higher (often exceeded the WHO 24-hour standard) in the dry season compared to the wet season across the different monitoring sites. Concentrations were also higher during nighttime compared to daytime in both seasons, and this difference was much more pronounced in the dry season. The fixed site in a significant industrial area, Shyampur, showed the highest concentrations compared to the other sites during both seasons, with a dry season average of approximately 290 ug/m3 and a wet season average of about 175 ug/m3. The real-time PM2.5 data will be further validated with filter-based gravimetric measurements for quality assurance. Filters are currently being analyzed for mass, black carbon, and chemical composition in a geochemistry laboratory. Incorporating source apportionment analysis and land use-based regression models of the datasets will support source identification. This will help to improve air pollution mitigation policies and implementation plans for reducing pollution while targeting its sources. The ultimate findings of this research will be conducive to assessing public health outcomes by incorporating socio-economic, demographic, and health data with the air quality data from this study, which is much needed in formulating an improved public health policy.

How to cite: Khan, R. H., Bayazid, A. R., Lee, M., Hasan, Md. K., Anonna, T. A., Rosenthal, L., Quayyum, Z., Barratt, B., and Baumgartner, J.: City-wide measurement of outdoor PM2.5 and black carbon to support evidence-based environmental policy in Dhaka, Bangladesh, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-138, https://doi.org/10.5194/egusphere-egu24-138, 2024.

09:45–09:55
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EGU24-400
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ECS
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On-site presentation
Pooja Chaudhary, Christopher P West, Raj Singh, Vinayak Sinha, Baerbel Sinha, and Alexander Laskin

The light-absorbing fraction of organic aerosols, commonly known as brown carbon (BrC), is a significant contributor to climate change. Biomass burning (BB) emissions of BrC are ubiquitous over Indo-Gangetic Plain (IGP). BB is very common in India because of biofuel usage for heating and cooking, agricultural residue burning, and uncontrolled garbage burning. Despite their abundance, the molecular-level understanding of BrC composition in IGP area is limited. Here, we investigate chemical composition of BrC collected at a suburban site in the northwest IGP. The aerosol samples were collected at the Atmospheric Chemistry Facility (30.667° N−76.729° E, 310 m above sea level) of the IISER Mohali, India. Compositional information was obtained by employing ambient ionization with high-resolution mass spectrometry. Specifically, Direct Analysis in Real-Time High-Resolution Mass Spectroscopy (DART-HRMS) was used to analyze samples of organic aerosols collected on the filter spots of 7-wavelength aethalometer. The samples were collected in two different seasons – post-monsoon, and winter. Post-monsoon season is dominated by largescale paddy residue burning whereas winter season is dominated by biofuel (wood and dungcakes) burning for heating and cooking purposes. Comparative analysis of optical records from the aethalometer and molecular information from DART-HRMS was used to assess the relationship between the chemical composition and optical properties of BrC.

How to cite: Chaudhary, P., West, C. P., Singh, R., Sinha, V., Sinha, B., and Laskin, A.: Chemical Characterization of Brown Carbon Aerosol Sampled in the Indo-Gangetic Plain Area., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-400, https://doi.org/10.5194/egusphere-egu24-400, 2024.

09:55–10:05
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EGU24-561
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On-site presentation
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Diya Mahmood, Firoz Khan, Deen Ahmed, Shahanaj Rahman, Abdul Motalib, Mohammad Moniruzzaman, Aftab Ali Shaikh, Nurul Huda, and Jing Xiang

This article delves into the climatological circulation patterns in South Asia, specifically honing in on the Indo-Gangetic Plain (IGP) region, and examines their repercussions on air quality and public health in Bangladesh. The investigation scrutinizes the seasonal dynamics of air masses, aerosol plumes, wind components, boundary layer conditions, ground-level air particles, and gases. Employing reanalysis data from ERA5 and visualizing it with the Grid Analysis and Display System (GrADS) between 2015 and 2021, the study corroborates its findings using ground-level observation data from various sites in Bangladesh. Uncovering that Bangladesh faces deteriorating air quality in winter due to transboundary influences from the IGP and the far western regions, the study notes elevated aerosol and gas levels at the northern Rangpur site compared to Dhaka, signifying transboundary air pollution impact. Conversely, clean air from the Bay of Bengal sweeps over Bangladesh during the South Asian monsoon, benefiting the entire western IGP region. The research identifies local processes contributing to winter aerosol levels in Bangladesh, with transboundary pollution, notably from coal and post-monsoon crop burning in India, exacerbating air pollution. Utilizing statistical generalized additive modeling (GAM), the study discerns relationships between air pollutants and meteorological variables. It reveals the influence of CO and SO2 emissions on winter PM 2.5 levels, while wind speed and the planetary boundary layer (PBL) show a negative correlation with PM 2.5 concentrations during the monsoon and post-monsoon seasons. The findings underscore the significant impact of biomass burning and the PBL on Bangladesh's air quality. Considering Bangladesh and the Maldives as particularly susceptible to poor air quality consequences, the study emphasizes the urgency of targeted interventions and adaptive strategies in these regions. Notably, it pinpoints hotspots in the North Indian, Pakistani, and Afghan regions, introducing a geopolitical dimension to the study. This underscores the transboundary nature of the issue, stressing the need for cross-border collaboration in finding solutions. Additionally, the study connects seasonal circulation patterns and air pollution sources to implications for air quality and public health in Bangladesh, proposing mitigation strategies. It suggests leveraging the Malé Declaration (MALE) as a catalyst for collaborative efforts in mitigating transboundary air pollution across South Asia. In summary, the research not only contributes insights into Bangladesh's air quality but extends its implications regionally, laying the groundwork for a comprehensive and collaborative approach to address shared air quality and public health challenges in South Asia.

How to cite: Mahmood, D., Khan, F., Ahmed, D., Rahman, S., Motalib, A., Moniruzzaman, M., Ali Shaikh, A., Huda, N., and Xiang, J.: Transboundary Air Quality Challenges in South Asia: A Comprehensive Analysis of Climatological Circulation Patterns and Implications for Bangladesh and Beyond, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-561, https://doi.org/10.5194/egusphere-egu24-561, 2024.

10:05–10:15
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EGU24-933
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ECS
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On-site presentation
Josfirin Uding Rangga, Eliani Ezani, Sharifah Norkhadijah Syed Ismail, Mohd Johari Mohd Yusof, and Shamsul Bahari Shamsudin

Today, more than 70% of the Malaysian population lives in urban areas, and this proportion continues to increase. Changes in land use and land cover, as well as additional anthropogenic factors, have altered the energy balance and led to spatio-temporal variations in urban climate. Active commuters who travel near roadways with high traffic density face a range of exposures, such as traffic-related air pollution (TRAP), traffic noise (TN), and urban heat (UHI). As urbanisation and population acceleration increase, understanding the environmental stressors faced by active commuters becomes crucial. Our aim is to measure the weekly spatial and temporal variation of TRAP, TN, and UH in selected cities in Malaysia and assess the health risks of exposures. The assessment was conducted along the selected routes in Kuala Lumpur (KUL) city centre and Cyberjaya (CYB) in Malaysia. The sampling campaign was conducted in October and November 2023. This comprehensive assessment measured TRAP (i.e., PM2.5, black carbon (BC)), TN, and UH simultaneously. The TSI SidePak (AM510), microAeth (MA200), TSI Edge 5 Personal Noise Dosimeter, and QUESTemp 36 models were used to measure PM2.5, BC, TN, and UH, respectively. The preliminary findings indicate a significant difference between peak hours (morning, noon, and evening) for all parameters (p < 0.001), except for BC concentrations (p = 0.37) during weekdays. All parameters were significant (p < 0.001) during weekends in KUL (Route 1). For CYB (Route 2), there was a significant difference in PM2.5, BC, TN, and UH levels (p < 0.001) between peak hours on both weekdays and weekends. PM2.5 (73.31 µg/m3), BC (6.19 µg/m3), and TN (80.10 dB(A)) were significantly higher on weekdays in Route 1 compared to Route 2, while the heat index was also higher (27.90 °C) in Route 1. Similar findings showed higher levels of PM2.5 (85.14 µg/m3), BC (5.95 µg/m3), and TN (77.89 dB (A)) on weekends in Route 1. Our study will help to address the knowledge gap on the impact of urban heat, air pollution, and noise pollution on climate adaptation among active commuters in urban cities. The findings of this study will contribute to the development of targeted interventions and strategies to enhance the resilience of these active commuters to the adverse effects of urban stressors. Our examination will also add to the wider discussion on sustainable urban mobility, highlighting the importance of specific actions to improve the environmental conditions for those who use active modes of transport. This will eventually encourage healthier and more sustainable travel options.

How to cite: Uding Rangga, J., Ezani, E., Norkhadijah Syed Ismail, S., Mohd Yusof, M. J., and Bahari Shamsudin, S.: Active Commuters’ Exposure to PM2.5, Black Carbon, Noise, and Heat During the Northeast Monsoon in Selected Urban Cities in Malaysia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-933, https://doi.org/10.5194/egusphere-egu24-933, 2024.

Coffee break
Chairpersons: Sarath Guttikunda, Nestor Rojas, Valeria Solórzano-Araque
10:45–10:55
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EGU24-954
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On-site presentation
Pieter G. van Zyl, Constance K. Segakweng, Catherine Liousse, Sylvian Gnamien, Eric Gardrat, Johan P. Beukes, Kerneels Jaars, Camille Dumat, Benjamin Guinot, Miroslav Josipovic, Brigitte Language, Roelof P. Burger, Stuart J. Piketh, and Tiantian Xiong

Health impacts associated with exposure to atmospheric aerosols are of global concern and are not completely understood. In addition, health studies are, especially, complicated in developing countries such as South Africa. Oxidative potential (OP), defined as a measure of the capacity of aerosols to oxidise target molecules, has been proposed as a viable alternative relevant biological metric to better quantify toxicological responses related to atmospheric aerosol exposure in health studies. The dithiothreitol (DTT) assay is the most commonly used method to determine OP of aerosols, which was used in this study to quantify the OP of outdoor and indoor atmospheric particulates collected at three low-income settlements in South Africa. This technique is easy-to-operate, low-cost, effective and reproducible. The DTT methodology had to be modified according to previous applications, which required choosing a suitable extraction procedure and -setup. The redox activity of size-resolved sampled aerosols was evaluated and related to their chemical composition with correlation analysis. The seasonal variations of DTT redox activity were established by normalizing in terms of aerosol mass and sampled volume for indoor and outdoor particulate samples. Higher redox activity was determined for the smallest aerosols (aerodynamic diameter <1 μm) compared to the larger particulates (aerodynamic diameter between 1 and 10 μm) in both environments. DTT redox activity correlated strongly with elemental- and organic carbon, as well as trace elements and water-soluble inorganic species. These correlations revealed toxic effects of sources of atmospheric aerosols in these settlements, which included domestic- and open biomass burning, vehicles and industrial activities. The DTT method was successfully applied in this study and could be used in other data scarce regions that are difficult to access.

How to cite: van Zyl, P. G., Segakweng, C. K., Liousse, C., Gnamien, S., Gardrat, E., Beukes, J. P., Jaars, K., Dumat, C., Guinot, B., Josipovic, M., Language, B., Burger, R. P., Piketh, S. J., and Xiong, T.: Quantifying the oxidative potential of aerosols in low-income urban areas in South Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-954, https://doi.org/10.5194/egusphere-egu24-954, 2024.

10:55–11:05
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EGU24-1236
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ECS
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On-site presentation
James Nimo, Jean R. Kubwimana, Chantal Umutoni, Pacifique Karekezi, Paterne Gahungu, Majid Ezzati, Allison Hughes, and Raphael E. Arku

Cities in sub-Saharan Africa (SSA) are undergoing significant economic and urban expansion. The rapid urban growth is shaping land use, housing, transportation, and energy for household and commercial use. Consequently, air pollution from diverse local and regional sources and with complex space-time patterns has emerged as a major environmental health concern for cities in SSA. Yet, limited city-scale data are a barrier to climate and health impact assessment as well as policy formulation and evaluation to reduce air pollution.  

We are implementing and testing the transferability of Pathways to Equitable Health Cities measurement protocol for Accra in Kigali, Rwanda. The protocol is designed to generate rich environmental pollution data in SSA cities. Both Accra and Kigali are representative of the rapid urbanization and economic transformation that are happening across SSA. We have assembled and integrated multiple low-cost, low-power, lightweight sensors that have been validated in prior studies to measure integrated and real-time fine particulate matter (PM2.5), black carbon (BC), and oxides of nitrogen (NO2 and NO) concentrations at city-scale. Initiated in November 2022, our year-long measurement campaign utilizes a network and combination of ‘fixed’ (n=10) and ‘rotating’ (n =120) monitoring sites. The sites represent variety of land uses and emission sources, including background, road traffic, commercial, industrial and residential areas, and neighbourhood socioeconomic classes. The fixed sites are monitored continuously for one year to capture temporal (annual and seasonal) patterns, whereas the rotating sites are monitored for one week (in groups of four per week) to capture spatial variations in the pollutant concentrations. In addition to the air pollutants, we are also collecting data on environmental noise and weather variables (i.e. temperature, relative humidity and wind speed/direction) to aid in the analyses. The Kigali initiative is being implemented in partnership with AIMS-Rwanda and the Rwanda Environment Management Authority (REMA) to promote capacity building within the government.  

Planned analyses involve the use state-of-the-art models, including spatial statistics, deep/machine learning approaches, to capture highly resolved temporal and spatial variations as well as socioeconomic inequalities in pollution levels across Kigali city and to identify sources and their relative contributions. The data form the basis of future climate change and air pollution forecasting and health impact assessment as well as policy evaluation and emission reduction scenarios in the city.

How to cite: Nimo, J., R. Kubwimana, J., Umutoni, C., Karekezi, P., Gahungu, P., Ezzati, M., Hughes, A., and E. Arku, R.: An integrated approach for generating rich city-wide air pollution data in growing Sub-Saharan African cities: Implementing transferable protocol in Kigali, Rwanda, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1236, https://doi.org/10.5194/egusphere-egu24-1236, 2024.

11:05–11:15
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EGU24-107
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ECS
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Virtual presentation
Muawiya Sani, Rabia Salihu Said, Tijjani Bello Idrith, and Sunusi Usman Yerima

Aerosol Optical Depth (AOD) is an essential parameter for understanding atmospheric aerosol distribution and its impact on climate and air quality. Satellite-based AOD retrievals play a crucial role in large-scale studies, but their accuracy necessitates validation against ground-based measurements. This study focuses on validating AOD data products from multiple satellite sensors (MODIS TERRA, MODIS AQUA, MISR, OMI and MERRA-2) using high-quality ground-based Aerosol Robotic Network (AERONET) AOD data within the West African region. The study aims to assess the performance of different satellite sensors in capturing the spatiotemporal variability of aerosol loading over West Africa for the period 2000–2022. Six AERONET stations (Banizoumbou, Cinzana, Ilorin, Dakar, Capoverde, and Koforidua) are considered within three sub-regions of West Africa (Sahel, Savannah, and Guinea Coast). Rigorous validation techniques, including intercomparison analysis and statistical metrics, are employed to evaluate the agreement between satellite-derived AOD and AERONET measurements. Preliminary results indicate that satellite retrievals generally capture the broad-scale patterns of aerosol distribution in West Africa. However, this study reveals significant discrepancies in some regions, emphasizing the need for improved satellite algorithms, especially in areas with complex aerosol properties. Furthermore, the analysis identifies the best-performing satellite sensors at each AERONET station when employing either daily or monthly data. Daily analysis revealed MODIS AQUA had the best agreement at Banizoumbou, Cinzana Capoverde and Koforidua, while MISR and MODIS TERRA performed best at Dakar and Ilorin, respectively. On the other hand, the monthly analysis revealed MODIS TERRA performs best at Banizoumbou, Dakar, and Capoverde stations, while MERRA performs best at Cinzana and Ilorin stations. MISR shows relatively lower performance compared to MODIS TERRA and MERRA at Banizoumbou and Koforidua stations. But surprisingly, MODIS AQUA wasn’t the best performer at any of the stations based on monthly analysis. These findings highlight the importance of enhancing satellite algorithms to improve the accuracy of aerosol retrievals and the importance of taking caution when selecting a type of satellite data product and the temporal resolution to use for climate studies, air quality monitoring, and environmental management in regions with intricate aerosol characteristics.

Keywords: Aerosol Optical Depth (AOD), MODIS TERRA, MODIS AQUA, MISR, OMI, MERRA-2, Aerosol Robotic Network (AERONET), West Africa.

How to cite: Sani, M., Salihu Said, R., Bello Idrith, T., and Usman Yerima, S.: Validation of Multiple Satellite Aerosol Optical Depth (AOD) Retrievals Using Ground-Based AERONET AOD Data over West Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-107, https://doi.org/10.5194/egusphere-egu24-107, 2024.

11:15–11:25
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EGU24-233
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ECS
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Virtual presentation
Elizabeth Mutua, Michael J. Gitari, August Andersson, Samuel M. Gaita, and Leonard Kirago

Air pollution is a major environmental human health risk in African cities, largely due to the rapidly growing urban population, unregulated traffic and industrial emissions, and inadequate regulations and pollution control policies. Currently, about a million premature deaths are linked to air pollution in Africa, and the related health burden is projected to increase. However, data on PM2.5 chemical characterization and source contribution, needed to address the air pollution challenges and inform policies, is currently limited and/ or inadequate for most African cities. In this view, year-round PM2.5 quartz filter samples were collected in Nairobi city and analyzed for mass concentration and PAHs (known for their carcinogenic and mutagenic properties). The average PM2.5 concentration was determined at 27 ± 6 µgm-3, exceeding the World Health Organization 24-h health guideline. The PAHs concentration ranged between 5 - 20 ng m-3 and were dominated by the heavy molecular weight PAHs (>4 rings). Molecular diagnostic ratios further revealed that the PAHs predominantly originate from combustion sources, such as traffic emissions. Overall, this study signal to a severe health concern, and provide information that can be exploited for policy formulation and air pollution mitigation strategies in Nairobi, as well as other African cities.

How to cite: Mutua, E., Gitari, M. J., Andersson, A., Gaita, S. M., and Kirago, L.: Unravelling the chemical speciation and sources of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Nairobi, Kenya, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-233, https://doi.org/10.5194/egusphere-egu24-233, 2024.

11:25–11:35
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EGU24-1194
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ECS
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On-site presentation
William Apondo, George Mwaniki, John Kennedy, Ivy Murgor, and Purity Munyambu

Air pollution poses a significant environmental risk in low- and middle-income countries (LMICs), characterized by diverse pollution sources and complex atmospheric processes. Specifically, the city of Nairobi grapples with air quality challenges, generating approximately 2800 tons of solid waste daily, with 75% undergoing collection and the remaining 700 tons (3.9KG/person/year) being openly burnt. Our study underscores the crucial role of bottom-up emissions inventories, particularly those associated with solid waste burning, in quantifying emissions and guiding evidence-based air quality management strategies within the context of data-scarce LMICs. Leveraging the Solid Waste Emission Estimation Tool (SWEET), our estimations encompass Methane (CH4), Black Carbon (BC), Particulate Matter (PM), oxides of Nitrogen (NOX), and other pollutants from municipal solid waste sources. Our baseline scenario, established for the year 2022, is compared with successive five-year alternative scenarios. Preliminary findings indicate alarming emissions, with around 5.2 million metric tons of PM10, 3,400 metric tons of SOx, and 300 million metric tons of Black Carbon annually from open burning under the business-as-usual scenario. Notably, implementing emission reduction strategies, such as the closure of official dumping site, exhibits promising outcomes. Projected reductions include up to 15% in Methane emissions by 2031 (Scenario1) and a substantial 75% reduction by 2033 (Scenario2). Furthermore, SOx, CO2, and PM emissions are anticipated to decrease by over 90% under these scenarios. Strategically reducing waste burning activities, coupled with measures like cutting the garbage truck fleet by 50%, could yield drastic emission reductions. Our findings emphasize the potential for impactful emissions reduction benefits in addressing open waste burning, an often overlooked source contributing significantly to air pollution in rapidly developing cities like Nairobi. The discussion highlights the importance of a bottom-up approach in developing emissions inventories to comprehend the impact of waste burning on overall city emissions reduction goals and incorporating Gender dynamics.

How to cite: Apondo, W., Mwaniki, G., Kennedy, J., Murgor, I., and Munyambu, P.: Unveiling the Crucial Role of Emissions Inventories from Solid Waste Burning in Air Quality Management across Diverse LMICs; A Case of Nairobi City, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1194, https://doi.org/10.5194/egusphere-egu24-1194, 2024.

11:35–11:45
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EGU24-16351
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ECS
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On-site presentation
Anamika Anand, Ryoichi Imasu, Kanako Muramatsu, and Prabir Patra

In India, agricultural burning, also known as  crop residue burning, is a significant source of air pollution, but quantifying PM2.5 emissions from these open burns has been a persistent challenge for researchers. Global fire databases actively relies on MODIS (Moderate Resolution Imaging Spectroradiometer)  for information on burn area, although MODIS has decades of fire activity data, the coarse spatial resolution (500 m–1 km) along with smog and hazy conditions in Punjab leads to inaccurate detection of fires and burn areas. In our previous work, we developed and implemented a deep learning-based segmentation model combined with Sentinel 2 imagery for accurate fire estimates from open burns and here we discuss our progress on region-specific domain adaptation of the model over Punjab.   
Initially, the model was trained on Sentinel-2 data from Portugal, utilizing the ICNF (Portuguese Institute for Nature Conservation and Forests) as reference data during 2016 - 2017, and subsequently tested on Punjab through a transfer learning approach.  Now, minimizing false detections by the pre-trained model requires region-specific adaptation; however, inadequate monitoring and lack of reference data in Punjab poses major challenges in the fine-tuning process. Therefore, we utilize ground level geolocated image data collected during our field observation campaign in Punjab along with Sentinel-2 and Google Earth data as an input to the pre-trained model. For the year 2020, over 1400 sites were identified as potential burn area polygons and integrated into vector data, with careful exclusion of aquatic bodies and urban areas to prevent false detections. The refined model processes this annotated data along with Sentinel-2 spectral bands B03 (green), B8A (Near Infrared), and B11 (Short Wave Infrared). Another significant challenge in conventional fire detection methods is identifying smaller burn areas. To address this, the model was deliberately trained with several smaller burned areas (up to 0.0256 hectares) in the  sample  to enhance the detection of smaller burns that often escape traditional methods. For validation, we intend to utilize  the onsite geolocated images taken during the campaign from different time periods. Model’s performance is evaluated using the Dice and IOU score method. Currently, the pre-trained model has an accuracy of 0.62 in Dice and 0.59 in IOU, with aspirations to elevate this accuracy to between 0.85 and 0.90.

As we await the outcomes of our ongoing analysis, we are enthusiastic to see how deep learning combined with high-resolution multispectral imagery of Sentinel - 2  can be used to fill missing gaps in burn area assessments. Our fire estimates and spatial burn area pattern for multiple years on Punjab,  will help us quantify the emission contribution from burning at local and regional level and help us understand how the emissions from the field are impacting the air quality in the nearby areas.  This enhanced methodology  aims to set the stage for creating a high-resolution fire emission inventory specifically for crop residue burning. The findings from this research will contribute significantly to understanding the impact of agricultural burning on air quality and may inform future studies and policy decisions.  

How to cite: Anand, A., Imasu, R., Muramatsu, K., and Patra, P.: Domain adaptation of deep learning  segmentation model for agricultural burn area detection using Hi-Resolution Sentinel-2 observations: A case study in Punjab, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16351, https://doi.org/10.5194/egusphere-egu24-16351, 2024.

11:45–11:55
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EGU24-122
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ECS
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On-site presentation
Sajesh Kuikel and Binod Pokharel

Wildfires, primarily over the Indo-Gangetic Plain, southern hilly region of Nepal, cause exceptionally high levels of pollution in the Central Himalayan region during the pre-monsoon season. Although wildfire smoke is one of the main sources of pollution, little research has been done on it. During the pre-monsoon season of 2021, we studied a hazardous level of air pollution in Kathmandu Valley, Nepal, using multiple datasets, including in-situ measurements, reanalysis data, and backward trajectory analysis. A total of 13 days exceeded the extreme pollution level of 134.29 μg/m3 (Mean + 2 * S.D.), with the highest daily concentration reaching 305 μg/m3. We found that smoke transported from nearby and transboundary wildfires was the main cause of the hazardous pollution in the valley. Furthermore, we discovered that the number of wildfires in the source region during that year was the highest on record. A strong correlation existed between daily active fire counts and valley pollution levels. The larger correlation among nearby locations indicates a greater responsibility for the increasing pollution, and it also reflects the rapid movement of pollutants into the valley. Due to the bowl-shaped structure of the valley, pollution accumulated and showed a considerable influence from nearby wildfires when it lagged by two days, reaching a maximum of 0.89 (p<0.05). Because of the calm wind conditions in the valley, a diurnal pollution pattern from the previous days persisted, although it was insufficient to entirely flush pollutants from the valley. Additionally, we noticed that synoptic and mesoscale dynamics in the area regulate the transport of pollutants to the valley. Since wildfire pollution affects people and economic activity in this region, conclusions drawn from research like ours may serve as a starting point for the implementation of legislation aimed at reducing the effects of wildfires and the air pollution they cause.

How to cite: Kuikel, S. and Pokharel, B.: The role of wildfires in surrounding regions in exacerbating air pollution in the central Himalayas of Nepal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-122, https://doi.org/10.5194/egusphere-egu24-122, 2024.

11:55–12:05
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EGU24-239
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ECS
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On-site presentation
Diksha Haswani and Ramya Sunder Raman

Fine particulate matter, PM2.5 - bound toxic metals upon uptake by the human body, plants and animals by various mechanisms pose health risks.  Additionally, they also play a role in altering the biogeochemical cycles of various species following dry/wet deposition onto soils, sediments and water bodies. Assessing the bioavailability of these metals using the total concentrations makes simplifying assumptions about the chemical states that these species are present in and the findings of risk assessments using such models/estimates are likely to have high uncertainties. Literature suggests that the sequential extraction procedure (SEP) is a versatile analytical method to examine the chemical fractionation of metals in particulate matter. Tessier’s SEP approach was used in this study to determine the extent of reactivity of metals in four fractions using specific solvents. Weekly composite of 24 hr integrated ambient PM2.5 samples (n=52+12 field blanks) collected onto Teflon filters, every other during 2019 were used.  These samples were collected at a regionally representative location in Bhopal, central India, as part of the COALESCE ambient aerosol measurement campaign.  Filter samples were utilized to quantify the metal fractions (K, V, Ti, Mn, Fe, Pb, Zn, and Cu) using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The internal check of the recovery of metals was determined by comparing the sum of metal fractions concentration with total metal concentration determined using a ED-XRF (Energy Dispersive X-Ray Fluorescence Spectrometer) on the same filters, prior to their leaching. The total sum of fractional concentrations were in good agreement with the bulk metal concentration (slope = 0.24-57.53, r2 = 0.92 -0.99) and the internal recovery ranged from 85%-110% for different analytes. Annual mean concentration of metal fractions of K, Ti, Pb and Zn were higher in the two bioavailable fractions including soluble, exchangeable fraction (F1) and carbonate, oxide and reducible fraction (F2) (43- 67 % of total concentration). Relatively higher proportions of Fe, Mn, and V were less bioavailable and were present in oxidizable fraction (F3) (40- 73 % of total concentration) indicating its high association with organic matter and inorganic sulfides. Cu was strongly bound to its silicate fraction and had its highest proportion present in the residual fraction (F4) (91 % of total concentration). As expected, application of the United States Environmental Protection Agency (USEPA) health risk assessment equations to the measured fractions revealed that the route of exposure for bioavailable metals was highest via the inhalation pathway, followed by dermal contact and ingestion. Total potential non-carcinogenic health risk indicator, the Hazard Quotients were below but close to the safe level of 1 for all bioavailable metal fractions. The cancer risk from bioavailable metal fractions were also within the USEPA acceptable limits for the three pathways. Overall, this work provides a database for bioavailable ambient PM2.5 heavy metals and evaluates potential health risks.  In the future, this work will be extended to assess the impacts of these metals on perturbing the biochemical cycles of these species in this regional environment.

How to cite: Haswani, D. and Raman, R. S.: Chemical fractionation and potential health risks assessment of particulate matter bound heavy metals in Bhopal, Central India , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-239, https://doi.org/10.5194/egusphere-egu24-239, 2024.

12:05–12:15
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EGU24-318
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ECS
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On-site presentation
High Resolution Transport Emission Inventory and Its Use in Locally Tailored Solution Strategies
(withdrawn)
Ritesh Kumar, Azra khan, Prakash Doraiswamy, Vandana Tyagi, Sanjar Ali, Bhavay Sharma, Kaustubh Chuke, Tammy Thompson, Abhinand Krishnashankar, Beatriz Cárdenas, and Ajay Singh Nagpure
12:15–12:25
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EGU24-469
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ECS
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Virtual presentation
Saadia Hina, Hamna Nisar, Salman Tariq, Muhammad Ibrahim, and Ammara Habib

Black carbon (BC), despite their small contribution in atmospheric aerosol loads, have growing attention for air quality, human health, and climate change implications. This study aims to investigate the long- term spatio-temporal trends of BC over various metropolitan cities in Pakistan through MERRA-2 reanalysis datasets ranging from 2001 to 2022. In addition, statistically significant spatial clusters (hotspots) of BC in Pakistan have been assessed through a geospatial statistical tool (Getis-Ord 𝐺𝑖) and finally, the hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model has been applied to identify the path and direction of BC. The increased trend of BC has been observed in winters due to low PBH (planetary boundary layer) and increased anthropogenic activities during this season. The decreased trends of BC were observed in summers due to precipitation and the washout process. Among the metropolitans of Pakistan, the highest values of BC concentration were recorded in Karachi while lowest values have been observed in Islamabad. The findings showed that most of the hotspot regions are in the southern region along with some central areas. The results demonstrate that BC concentration in Pakistan rises annually because of increased biomass burning, vehicle emissions, and transboundary air pollution. It is anticipated that our study will furnish valuable insights for assessing the hotspots of BC along with their local and remote sources across Pakistan.

How to cite: Hina, S., Nisar, H., Tariq, S., Ibrahim, M., and Habib, A.: Insights into long-term variations of Black carbon over various metropolitans in Pakistan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-469, https://doi.org/10.5194/egusphere-egu24-469, 2024.

Lunch break
Chairpersons: Rebecca Garland, Nestor Rojas, Sandra Maldonado
14:00–14:10
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EGU24-664
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On-site presentation
John Richard Hizon

Air pollution accounts for 2.2 million premature deaths in the Western Pacific Region. In the Philippines, mortality rates related to poor air quality reached 45.3 deaths for every 100,000 people in 2018. Apart from the cost to lives, pollution has cost the country US$ 1B in 2015, which accounts for 0.3% to 0.4% of the country’s gross domestic product.

Despite the cost to life and associated economic burden, the current system for sampling air pollution data - both indoor and outdoor in urban areas - has not provided dense spatiotemporal distribution information to identify air pollution hotspots.

While the sensor installations of the government and private sector in some parts of the country provide data on the current status of air pollution, one air quality sensor system per city - the average in the Philippines - provides very little information on how pollution is distributed and may not be able to identify hotspots that increase the exposure of the general public.

To close the gap in the strategic installation of air quality sensor systems and the availability of actionable data on air quality status, the University of the Philippines’ College of Engineering proposed the UP Center on Air Quality Research in Urban Environments (UP CARE) research program for government funding. This program provides a venue for engineers, scientists, health professionals and other domain experts to solve this collective challenge on air pollution.

 Our talk will focus on UP CARE’s efforts to study how pollutants, both gaseous and particulate matter, are dispersed in urban environments. One key component in this program is the development of an IoT infrastructure and its online platform that connects various locally-developed wireless sensor nodes to measure air pollutants generated by vehicles traversing the streets of central business districts and determine how much of these pollutants enter homes, schools, and offices. In addition, the online platform would include mobile sensing modalities in our trains, buses, and cars plus personal measurements through wearables to complete the cycle of exposure to air pollution parameters one encounters daily. The goal is to integrate these measurements into a resilient and scalable IoT platform that will enable the development of applications that the general public could use to mitigate their personal exposure to air pollution and allow the local agencies to devise a more reliable approach/plans to decrease the risk of their constituents and stakeholders. The ever-expanding database of measurements will provide datapoints for researchers to improve dispersion and forecasting models and improve our understanding of the long-term effects of air pollution to one’s health.

How to cite: Hizon, J. R.: Air quality data for all: The case for air quality research in the Philippines, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-664, https://doi.org/10.5194/egusphere-egu24-664, 2024.

14:10–14:20
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EGU24-829
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ECS
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Virtual presentation
Pooja Manwani, Nirav Lekinwala, Mani Bhushan, Chandra Venkataraman, and Harish Phuleria

The PM2.5 concentrations in Northern India are one of the highest in the world, posing significant risks to human health and affecting climate and air quality. This study aims to assess the influence of different sources of emissions and meteorological conditions on the levels of PM2.5 at a regional background location in Northern India. The study employed a combination of the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP), together with Positive Matrix Factorization (PMF), to assess the effects of various factors on PM­2.5 pollution. The RF model accurately captured the variation of PM2.5 (R2 = 0.95) during the sampling period. The results show that emissions sources and meteorology accounted for approximately 79% (99.8 μg/m3 ± 68.9) and 21% (26.5 ± 18.3 μg/m3) of the variability in PM2.5 levels, respectively. Secondary aerosols (SA) had the most significant influence among all sources, accounting for around 45.7% and having a SHAP value of approximately 23.6 μg/m3. Biomass burning had the second highest impact, contributing around 23.1% and having a SHAP value of approximately 19.3 μg/m3. The RF-PDP approach was utilized to assess the sensitivity of the combined influence of secondary aerosols and biomass burning on PM2.5 concentrations. The results suggest that controlling concentrations of secondary aerosols below 25 μg/m3 and biomass burning below 15 μg/m3can reduce the overall PM2.5 concentration by over 2.5 times. It is to be noted that even after the strategic control measures, PM2.5 concentrations are predicted to be over 100 μg/m3. Given the critical role of secondary aerosols in PM2.5 pollution and the complexity of their generation mechanisms, the temporal variations of SA concentrations and their drivers were also analyzed via RF-SHAP during the study period. The model results highlight that secondary aerosol formation is mostly driven by meteorological conditions (64% ~ 13.6 ± 18.5 μg/m3) than primary emissions (36% ~ 7.7 ± 10.4  μg/m3), making it difficult to implement control strategies due to dependence on meteorological conditions. However, the sensitivity analysis using RF-PDP suggests that under favourable meteorological conditions, strategic control of primary emissions like biomass burning and coal combustion can reduce the secondary aerosol concentration and consequently reduce particulate pollution. In conclusion, the findings aid in uncovering approaches to effectively mitigate particulate pollution by managing emissions during favourable meteorological situations. Thus, the integration of machine learning algorithms with expert decisions and existing methodology can assist in effectively addressing ambient air pollution and find extensive use in the field of air pollution.

How to cite: Manwani, P., Lekinwala, N., Bhushan, M., Venkataraman, C., and Phuleria, H.: Unravelling the Nexus of emission sources and meteorology on Regional PM­2.5: A Comprehensive Analysis Using Source Apportionment Model and Machine Learning for Effective Pollution Mitigation Strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-829, https://doi.org/10.5194/egusphere-egu24-829, 2024.

14:20–14:30
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EGU24-1151
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ECS
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On-site presentation
Ayush Dwivedi, Ayu Parmar, and Sachin Chaudhari

Air pollution, primarily driven by particulate matter (PM), significantly threatens public health. India, with three cities ranking among the world's top ten most polluted and with PM concentrations exceeding WHO guidelines by almost 11 times, urgent measures are needed to address this escalating crisis. AirIoT, a densely deployed IoT-based air quality monitoring network in Hyderabad, India, is an evidence-based approach to bringing awareness and increasing public participation by alleviating data scarcity.

PM concentration has high spatial variability and is often characterized by data scarcity since traditional monitoring setups fall short due to their bulkiness and cost limitations. To tackle this, our research advocates for an innovative approach—deploying a dense network of IoT-enabled PM monitoring devices equipped with low-cost sensors. The work revolves around two core elements: measurement and modelling. 49 IoT-based PM monitoring devices were developed, calibrated, and deployed across Hyderabad, India, covering urban, semi-urban, and green areas. Calibration, essential for seasonal variations, utilized a precise reference sensor. A web-based spatial data dashboard and an Android app were also developed for dynamic geo-visualization of the data from the IoT network, offering citizens and governments actionable insights for efficient pollution control measures. Spatial interpolation models were also designed to extrapolate measurements at micro and macro levels. Demonstrating the effectiveness of dense deployment, a case study was conducted during the Diwali festival, highlighting the importance of localized information in scenarios with air pollution hotspots.

The health impacts of air pollution were also studied, correlating measurements with respiratory, cardiovascular, and psycho-physiological effects. A pilot study utilizing data from AirIoT, health wearables and a questionnaire investigated the long-term health implications for security personnel exposed to air pollution. Computer vision-based methods were developed to scale air pollution monitoring using features like visibility, traffic type and density, eliminating the need for frequent sensor usage. Trained on a large dataset using deep learning, these methods predict air quality in real-time, offering a viable alternative for large-scale implementations.

Ensuring citizen engagement and capacity building, we conducted pilot studies in schools, engaging students in understanding and combating air pollution. Public display systems showcasing real-time pollution levels generated excitement and awareness, leading to residents advocating for reforms. Engineering students from various colleges were trained through hackathons and internship programs to develop low-cost air pollution monitoring devices for local measurements at their institutions and localities. Our work extends beyond air quality monitoring, interlinking with broader smart city applications through the Smart City Research Center at IIITH. Utilizing interoperability standards, such as oM2M, we integrate air quality data with other verticles like weather, water, energy, and crowd monitoring, establishing an interoperable environment with scalable prototypes for other smart cities. Finally, embracing the importance of data sharing and management, our live data feeds into the Indian Urban Data Exchange (IUDX), a data exchange platform aligning with the Smart Cities Mission in India. With a blend of IoT technology and social participation, this collaborative initiative ensures a comprehensive and data-driven approach to address the complex challenges of air pollution in India.

How to cite: Dwivedi, A., Parmar, A., and Chaudhari, S.: Enhancing Air Quality Monitoring in India through Dense IoT Deployments (AirIoT): A Multi-faceted Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1151, https://doi.org/10.5194/egusphere-egu24-1151, 2024.

14:30–14:40
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EGU24-3111
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On-site presentation
Bertrand Tchanche, Sotirios Papathanasiou, and Anil Namdeo

Africa is experiencing a high urbanization rate, ~4% with megacities emerging like Cairo in Egypt, Lagos in Nigeria, Kinshasa in Democratic Republic of Congo. In parallel, a deterioration in air quality is being witnessed. Road traffic contributes significantly to atmospheric pollution through unregulated traffic, poor roads’ design, poor fuel quality and surge in vehicle imports. Arica is the continent with lowest roads densities and with most unpaved roads. Congestion is now frequent in cities with adverse consequences on the economy, health, and society. High daily temperature observed in tropical climates, and favourable wind speed favour dust resuspension and Sahara dust dispersion. Literature review shows a less focus on in-cabin dispersion and impacts. The present work regards a study of the indoor environmental quality (IEQ) of the public transport buses provided by the AFTU company in the city of Thiès, in Senegal. A Particle Plus 8301-AQM2 Series handled optical particle counter (OPC) was used as it offers a good characterisation of the fine particles. the outdoor air, the vehicle itself and the occupants were identified main pollutants sources. Fine particles concentrations, carbon dioxide (CO2), temperature and relative humidity were recorded on several routes at different periods of the day and for several days during the rainy season. Recorded data show high concentration of PM2.5 which increases over time (from 25 up to 300 µg/m3) depending on outdoor conditions and the areas crossed by the vehicle. Variations of PM concentration in different channels: 0.5, 1, 2.5, 5 and 10 were also analysed. Recorded values showed very small mass fraction of 0.5 and large proportion of 5-10 µm diameter particles. CO2 concentration (300-900 ppm) varies with the number of passengers during the trip. The temperature was above 30 °C and the relative humidity, in the range 40-70%. The speed analysis shows high frequency variations and was found low, ~2.5 m/s. A conclusion that emerged is that keeping doors and windows open help in eliminating excess CO2 but ends in high level of particulate matter concentration in the cabin.

How to cite: Tchanche, B., Papathanasiou, S., and Namdeo, A.: Dust Characterization and Evaluation of Indoor Environmental Quality (IEQ): Case Study of AFTU Buses in Senegal , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3111, https://doi.org/10.5194/egusphere-egu24-3111, 2024.

14:40–14:50
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EGU24-7641
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On-site presentation
Pallav Purohit, Markus Amann, Gregor Kiesewetter, Wolfgang Schöpp, Fabian Wagner, Zbigniew Klimont, Chris Heyes, Adriana Gomez-Sanabria, Parul Srivastava, and Jens Borken-Kleefeld

South Asia is a global hotspot of air pollution, harboring 37 of the world's 40 most polluted cities. Sixty percent of its residents inhabit areas characterized by high pollution levels, where concentrations of fine particulate matter (PM2.5) - accountable for chronic respiratory diseases and over two million premature deaths annually in the region - surpass the least stringent air quality standard set by the World Health Organization (WHO). Addressing this problem with fragmented approaches is unlikely to yield significant results, as air pollution extends beyond geographical boundaries. Even if fully executed, existing policy measures will only offer partial relief in diminishing PM2.5 concentrations in South Asia.

This study aims to identify and map air pollution hotspots in South Asia in terms of concentration and exposure, understand the various sources of pollution in hotspot areas, and help categorize policy actions and interventions based on a systematic analysis of costs and benefits using the GAINS modeling framework. A large variety of emission sources contribute to PM2.5 pollution in ambient air therefore, effective air quality management needs to balance measures across these sources. Our results reveal that the current environmental policies will decouple emissions from economic growth, however, will not be sufficient to deliver large reductions in ambient PM2.5 in the South Asia region. There is scope for further measures beyond current policies that could approach the WHO Interim Targets (35 µg/m3) for PM2.5. Finally, cost-optimal strategies for air quality management can achieve significant cost savings compared to conventional approaches; however, they require cooperation within states, regions and countries in South Asia.

Monitoring of the chemical composition of PM2.5 reveals that a significant share of total fine particulate matter in ambient air in South Asia is composed of secondary particles, i.e., particles that are formed in the atmosphere through chemical reactions from gaseous precursor emissions (i.e., SO2, NOx, NH3 and VOC). This is relevant for air quality management, as measures that only address sources of primary particles often will not affect these secondary particles and thus have only a limited impact on total PM2.5 concentrations in the atmosphere. Cost- effective air quality management must also include measures for the precursor emissions of secondary particles. Some legislation exists for SO2 and NOx emissions, but its effectiveness can be enhanced by also including ammonia emissions (mainly from agricultural sources) in the portfolio as in many situations they are critically determining the generation of secondary particles.

This study examines four scenarios aimed at mitigating air pollution, varying in terms of policy implementation and international collaboration. The most economically efficient scenario, characterized by full coordination among states/provinces, regions, and countries within South Asia, would lead to a reduction in the average PM2.5 exposure in the region to 30 μg/m³ by 2030. Implementation of this cost-effective scenario is projected to annually prevent 750,000 premature deaths by 2030. The developed scenarios are integrated into the World Bank's support for crafting regional air quality management plans at both state/province and regional (i.e., Indo-Gangetic Plain) levels in South Asia.

How to cite: Purohit, P., Amann, M., Kiesewetter, G., Schöpp, W., Wagner, F., Klimont, Z., Heyes, C., Gomez-Sanabria, A., Srivastava, P., and Borken-Kleefeld, J.: Cost-effective emission reductions to improve air quality in South Asia , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7641, https://doi.org/10.5194/egusphere-egu24-7641, 2024.

14:50–15:00
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EGU24-18168
|
Virtual presentation
Kamlika Gupta, Victor Chang, Mohan Yellishetty, and Harish Phuleria

Introduction and background

Opencast coal mining accounts for more than 85% of coal production in India. Heavy transportation and mining activities have been identified as a significant contributor to the emission of fugitive dust and fine particulate matter (PM2.5) leading to a decline in air quality and negative health impacts within densely populated coal mining regions. PM2.5-bound species which are not routinely monitored such as elemental carbon (surrogate for diesel particulate matter, a Class I carcinogen), toxic metals and PAHs (poly aromatic hydrocarbons) may pose significant risk to the inhabitants around these areas raising serious health and regulatory concerns.  

Methodology

PM2.5 samples were collected from roadsides and residential sites near an active coal mining area in Eastern Maharashtra, India at 2, 5 and 10 kms from the mine site comprising of a typical coal haul roadside, an urban roadside, and residential locations. Collected PM samples were analysed for elemental and organic carbon (EC & OC) through thermo-gravimetric analysis, water-soluble metals through ICP-MS and PAHs through GC-MS. Carcinogenic and non-carcinogenic risk was estimated for these species and PM toxicity was measured through acellular assays (Dithiothreitol and Ascorbic Acid).  

Results and conclusions

Five-folds higher PM2.5 exposure levels (~500 ± 190 µg/m3) were observed near coal-haul road than at the residential sites. PM2.5 concentration at 2 and 5 km residential sites was comparable (100-120 µg/m3), but 2-3 times lower at the 10 km site and the residential background location. Average DPM concentration (measured as EC) across the sites was 11.3 ± 12.2 µg/m3, with 3 times higher levels at the coal haul road due to the dominance of diesel-powered trucks. Cr (20 ± 2.3 µg/m3), Ni (5.6 ± 0.3 µg/m3), Cd (7.2 ± 1.5 µg/m3), As (2.8 ± 1.6 µg/m3), and Pb (3.7 ± 0.9 µg/m3) emerged as important carcinogenic metals across the sites likely attributable to coal combustion and vehicular exhaust. Average levels of Benzo(a)pyrene, a priority pollutant, was observed to be 15 ± 2.3 ng/m3 at the community sites. Toxicity of PM—measured as OPvolAA and OPvolDTT—was higher at the roadside (2.3 ± 0.6 nmol min-1 m-3 and ~1.9 ± 0.8 nmol min-1 m-3 respectively) compared to residential sites (~0.76 ± 0.01 nmol min-1 m-3 and 0.94 ± 0.2 nmol min-1 m-3 respectively) against the background 0.6 ± 0.2 nmol min-1 m-3 (OPAA) and 0.9 ± 0.12 nmol min-1 m-3 (OPDTT). The inhalation risk for PM2.5 was observed to be 4 x 10-4 indicating a significant risk to the population. The study highlights the high exposure to PM2.5 and potential health risks in communities around opencast coal mines. It further underscores the need for considering PM composition and toxicity in environmental regulations to safeguard public health from the adverse impacts of industrial activities in data scarce low- and middle-income countries.  

How to cite: Gupta, K., Chang, V., Yellishetty, M., and Phuleria, H.: PM2.5 exposure characterisation around opencast coal mines: leveraging health risk and toxicity assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18168, https://doi.org/10.5194/egusphere-egu24-18168, 2024.

15:00–15:10
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EGU24-14179
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ECS
|
Virtual presentation
Abinaya Sekar, Ada Wright, Victor Nthusi, and Pallavi Pant

Air pollution is the leading environmental risk factor for human health, with millions of deaths annually and significant societal and economic costs. While access to the latest, reliable, and free air quality and health data is vital for informed decision-making, data on air pollution remains limited, especially in low- and middle-income countries (LMICs). The State of Global Air Initiative (SoGA), a collaboration between the Health Effects Institute and the Institute for Health Metrics and Evaluation, addresses this need by presenting comparable data on levels and trends of air quality and the associated health impacts for more than 200 countries and territories and more than 7000 cities around the world.

The data are drawn from the Global Burden of Disease (GBD) Study and include data on exposure to air pollutants - fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) and the associated burden of disease – deaths, death rate and disability adjusted life years (DALYs) as well percentage of deaths attributed to specific causes of disease and death. To enhance accessibility and reach, the information is presented in a variety of ways including reports, factsheets, an interactive data app, story maps, and videos, often in multiple languages. All the reports and data resources are accessible via https://www.stateofglobalair.org/.

These estimates are produced using a variety of data - air quality estimates are produced from a combination of data from over 10,000 ground-based monitors, satellite observations, and outputs from GEOS-Chem, a chemical transport model. The disease burden estimates are produced using country-specific death and morbidity rates, other health data including disease incidence, population demographics, and exposure-response curves derived from epidemiological studies. However, since the estimates rely on available data on air quality and health, in some cases, estimates are uncertain, particularly in parts of Asia and Africa where data gaps remain. A new dataset featuring estimates for the year 2021 is set to be released shortly. Despite the caveats, such information can be used for public engagement and for making evidence-based decisions to improve air quality and public health.

In 2022 alone, the SoGA initiative reached audiences in more than 50 countries, and for many countries, they are the only available estimates for air pollution levels and associated health impacts. The data have been used for public engagement, media reporting and to inform policy decisions, especially in LMICs. These data are also relevant for scientific research.

Overall, the SoGA initiative serves to close the gap between scientific research and public understanding on air pollution and its health impacts. In this presentation, we will showcase data from the SoGA platform, review lessons learnt and highlight opportunities for future research and engagement.

How to cite: Sekar, A., Wright, A., Nthusi, V., and Pant, P.: The State of Global Air Initiative: Increasing Access to Data on Air Quality and its Health Impacts , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14179, https://doi.org/10.5194/egusphere-egu24-14179, 2024.

15:10–15:20
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EGU24-8954
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On-site presentation
Jayaprakash Murulitharan, Alex Archibald, and Chiara Giorio

The Greater Kuala Lumpur (GKL) is the most urbanised region in Malaysia that experiences persistently high levels of PM2.5. Transboundary haze caused by biomass burnings in Sumatra, Indonesia, exacerbates the PM2.5 concentration levels in GKL, negatively impacting the public's health and the socioeconomic environment. We aim to investigate the specific influence of fires induced by biomass burnings in Sumatra provinces, namely Jambi, Riau, and South Sumatra, on PM2.5 concentration levels in GKL during the transboundary haze of September 2019. Our research addresses four key objectives: i)analysing PM2.5 pollution level during 2019 in GKL at Bangi (BG), Batu Muda (BM), Cheras (CS), Klang (KG), Kuala Selangor (KS), Putrajaya (PA), Petaling Jaya (PJ), and Shah Alam (SA); ii)estimating PM2.5 emissions from biomass burnings in Sumatra in 2019; iii)determining smoke pathways during biomass burning events in September 2019; and iv)estimating the contribution of Riau, Jambi and South Sumatra provinces towards PM2.5 concentration load in GKL during September 2019. Our analysis revealed that in September 2019, 80% to 100% of days in GKL exceeded the Malaysian Air Quality Standard (MAQS) and the World Health Organization (WHO) daily guideline for PM2.5 levels. We utilised the Global Fire Assimilation System (GFASv1.2) biomass emission inventory to estimate the total PM2.5 mass emitted from biomass burning events within our domain, latitude 5oS to 10oN and longitude 95oE to 110oE. Our estimation showed that 1.78 teragrams (Tg) of PM2.5 mass was emitted from biomass burnings within the study region in 2019, with 55% (0.90Tg) of these emissions occurring in September. Spearman's analysis demonstrated a strong positive correlation (ρ = 0.747, p < 0.001) between PM2.5 mass emissions from Sumatran biomass burnings and elevated PM2.5 concentration levels in GKL in September 2019. Emissions from Jambi, Riau and South Sumatra provinces accounted for approximately 94% of the total PM2.5 mass emitted during September 2019. Using the Numerical Atmospheric-Dispersion Modelling Environment (NAME) backward run, we observed that the southwestern air pathway influenced the transport of smoke-induced by biomass burning from Jambi, Riau, and South Sumatra towards GKL throughout September 2019. By integrating the NAME backward run with GFASv1.2, we simulated the PM2.5 concentrations in GKL that originated from biomass burnings in these three provinces. The NAME-GFAS model exhibited a slight underestimation (mean bias: -3 µgm-3 to -12 µgm-3) compared to biomass-induced  PM2.5 concentration levels in GKL at the eight locations in GKL in 2019. Notably, the model and observations demonstrated good agreement at these locations for September (correlation coefficient: 0.62– 0.70). Our model predicts that fires from Riau and Jambi provinces collectively account for 97% of  PM2.5 concentration levels in GKL during transboundary haze. Of these, fires from Riau dominated PM2.5 concentration levels in KG (56%), KS(51%) and BG (56%). While Jambi contributed mostly to BM(57%),PJ(51%),CS(55%) and PA (44%). SA has equal contributions from Riau and Jambi. South Sumatra consistently contributed between 1-3% at the respective stations. These findings stress the urgency of considering the effect of geographical morphology in addressing elevated PM2.5 levels during transboundary haze events in GKL.

How to cite: Murulitharan, J., Archibald, A., and Giorio, C.: Uncovering the effect of fires in Jambi, Riau, and South Sumatra on PM2.5 concentration levels in Greater Kuala Lumpur during September 2019 transboundary haze pollution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8954, https://doi.org/10.5194/egusphere-egu24-8954, 2024.

15:20–15:30
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EGU24-14441
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ECS
|
Virtual presentation
Ayako Kawano, Makoto Kelp, Minghao Qiu, Eeshan Chaturvedi, Sunil Dahiya, and Marshall Burke

India has experienced elevated levels of Particulate Matter (PM) 2.5 concentrations. Despite increased efforts by the Indian government,  the current monitoring network remains limited, impeding a comprehensive understanding of PM2.5 variations throughout the country. Limited PM2.5 data has led previous health studies to rely on publicly-available monthly PM2.5 estimates. However, these estimates have large uncertainties over the under-monitored regions, including India because PM2.5 observations have been calibrated into their model. The coarse temporal resolution of existing datasets makes it challenging to assess short-term effects of exposure to PM2.5. To bridge these gaps, it is imperative to develop daily PM2.5 datasets with robust spatial and temporal certainty.

This study develops open-source daily PM2.5 datasets at a 10 km resolution for India spanning almost two decades (2005 - 2023). Leveraging two-stage machine learning model with 10-fold spatial cross-validation (CV), we generate PM2.5 estimates for regions without ground measurements. In contrast to random k-fold CV, widely used in previous studies, spatial CV is implemented in this study to control for spatial auto-correction, which could lead to overfitting to the training data and underestimation of spatial prediction errors. The first stage fills missing observations for daily MODIS AOD, Sentinel-5P mission's TROPOPOMI NO2, and TROPOMI CO. The second stage predicts daily ground-measured PM2.5 concentrations. Two models are constructed for the second stage: the AOD model and the Full model, the latter incorporating TROPOMI features in addition to AOD.

The Full model exhibits a spatial out-of-sample performance with an R2 of 0.68, effectively predicting local and temporal PM2.5 variations rather than just average differences between locations, months, or years (within R2 = 0.49). The AOD model performs similarly, with an R2 of 0.64 and within R2 of 0.45. At the monthly level, our model outperforms the existing monthly PM2.5 dataset, with an R2 of 0.74 and within R2 of 0.52. 

Utilizing our PM2.5 predictions, we identified that 31% of 10 km grid cells across the country demonstrated a more than 5% reduction in PM2.5 concentrations in 2018-2022 compared to 2005–2010, and any decrease in PM2.5 was observed in 75% of the locations. Additionally, population-weighted annual average PM2.5 concentrations indicate a decline since 2018, except for a notable increase in 2021. Despite an overall declining trend since 2018, approximately 60% of the population remains exposed to PM2.5 concentrations above the national annual guideline (40 µg/m3), with 10% facing extreme levels of 80 µg/m3 annually.

Our method is useful for resource-constrained countries to understand nationwide air quality trends and identify areas with elevated pollution. To address this, we established the optimal number of air quality monitors using multiple machine learning models with randomly-sampled incremental training data. Our findings show a polynomial increase in within R2 for test data, ranging from 0.24 at 25 monitors to 0.54 at 300 monitors in the training data.

Our predictions offer valuable insights into air quality trends in India from 2005 to 2023. Importantly, our estimates contribute to understanding the number of ground monitors needed to explain variations in PM2.5 concentrations across the country, offering insights for other countries.

How to cite: Kawano, A., Kelp, M., Qiu, M., Chaturvedi, E., Dahiya, S., and Burke, M.: Bridging Data Gaps for Air Quality Monitoring: Daily PM2.5 Estimates for 10 km Grid Cells in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14441, https://doi.org/10.5194/egusphere-egu24-14441, 2024.

15:30–15:40
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EGU24-1214
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ECS
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Virtual presentation
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K. Santiago Hernández, Duvan Nieves, Jhayron S. Pérez-Carrasquilla, Paola Montoya, Manuel D. Zuluaga, and Mauricio Ramírez

Machine Learning (ML) techniques have acquired great importance for forecasting air pollution events, due to their relatively low computational cost and skillful results within horizons of up to 3 days. In this study, we forecast PM2.5 concentrations measured by low-cost sensors in the Aburrá Valley, a densely populated and complex terrain region in the Colombian Andes. ML models such as Artificial Neural Networks, Random Forest, Gradient Boosting, and Support Vector Regression, were trained for each forecast horizon (up to 72 hours) using data from satellites and global atmospheric models, which are available in other cities with little in-situ information. The information includes 2-meter temperature, boundary layer height, latent heat flux, winds at different levels and precipitation from the Global Forecasting System (GFS); total aerosol optical thickness (AOD), dust AOD, black carbon AOD and sea salt AOD data from the CAMS Global Atmospheric Composition Forecast; and an index calculated from predicted back-trajectories and the fire radiative power derived from MODIS satellite-monitored hotspots, which allows accounting for long-range transport of biomass burning aerosols. As an added value, we investigated the effect of including data from real-time PM2.5 concentrations from low cost sensors, as well as operational forecast information from the Early Warning System of Medellín and the Aburrá Valley (SIATA) with the WRF regional model. The predictions were evaluated across multiple performance metrics and during an air quality special period in which air pollution increases in the region. Our results show that ML-based forecasts perform better than those obtained directly from CAMS. By including real-time measured information, forecast performance significantly improves during the first 24 hours after initialization. In addition, meteorological data obtained from the WRF model are useful for extending the usefulness of the forecasts to longer horizons (2 to 3 days). Since this approach is based on satellite data and global atmospheric models, it can be easily replicated in other cities with scarce in-situ information. Finally, this work highlights the usefulness of these tools for air quality management and serves as a reference framework for the implementation of forecasting tools in other cities with scarce air quality data.

How to cite: Hernández, K. S., Nieves, D., Pérez-Carrasquilla, J. S., Montoya, P., Zuluaga, M. D., and Ramírez, M.: Forecasting PM2.5 concentrations using machine learning approaches: added value of low-cost monitoring and regional modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1214, https://doi.org/10.5194/egusphere-egu24-1214, 2024.

15:40–15:45

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall X5

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Sarath Guttikunda, Sebastian Diez
X5.84
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EGU24-904
Current Status of Air Quality Monitoring in Rwanda Combined with Satellite Analysis and the Capacity Building within the Finkerat Project
(withdrawn)
Pie Celestin Hakizimana, Beatha Akimpaye, Blaise Dushime, Jean Calude Mucyo, Anu-Maija Sundström, Henrik Virta, Seppo Hassinen, Katja Loven, Jutta Kesti, Anne Hirsikko, and Harri Pietarila
X5.85
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EGU24-17
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ECS
Indoor PM2.5 Exposure, Source Contribution and Related Health Risks During Haze and Non- haze Episodes in Dhaka Cantonment, Bangladesh: A Comparative Study
(withdrawn after no-show)
Samiha Nahian, Farah Jeba, Tasrina Rabia Choudhury, Bilkis Ara Begum, and Abdus Salam
X5.86
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EGU24-481
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ECS
Dr Gabriel Friday Ibeh, Dr Lawrence M Ibeh, Dr Vincent N Ojeh, and Dr Daniel Okoh

This research present methane distributions and mitigation innovative strategies for environmental sustainability. Satellite methane data from 2015 to 2022 at three locations across Nigeria will be used in this study. Artificial neural network application will be adopted for data modelling and analysis. Methane is a significant greenhouse gas (GHG) and its atmospheric concentration has virtually increased globally due to its anthropogenic sources such as rice farming. Rice farming is one of the engines of economic growth and economy development in Nigeria, at the same time responsible for significant portion of global methane emission which poses risk as a health burden to the environment. The study will investigate the distributions of methane emission within the locations and years of study. Reduction in rice farming will reduce methane and will have negative impact in economy development.  But, if managed properly for environmental sustainability through relevant innovations and technologies, productive rice will be achieved. To mitigate these emissions, there are several policy responses and innovations that can be implemented. The innovative strategies such as microbial process, Happy Seeder Machine approach, changing of rice production practices, integrated agronomic management strategies and other policy approaches can  mitigate and regulate methane emissions from rice farming at the same time have a proper produce. Details application on how these innovative strategies will be used to mitigate and regulate methane emmissins for environmental sustainability will be discussed in this study.

How to cite: Ibeh, D. G. F., Ibeh, D. L. M., Ojeh, D. V. N., and Okoh, D. D.: Analysis and Policy Response to Mitigate Methane Emissions from Rice Farming for an Environmental- Sustainability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-481, https://doi.org/10.5194/egusphere-egu24-481, 2024.

X5.87
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EGU24-645
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ECS
Chantelle Howlett-Downing

Source apportionment by means of factorization is the traditional means of assigning of sources to factors in air pollution studies. Both PCA and DN-PMF have assumptions, strengths, and limitations. In common these two models have the assigning of sources to factors which can be subjective and dependent on limited information. PCA and DN-PMF is performed on data from three cities which were sampled at the same time, 16 April 2017 to 18 April 2018. The PCA will determine how many factors are assigned and the DN-PMF will determine the assigning of sources to factors. The geographical origin of the air particulates was modelled by means of HYsplit. The DN-PMF was able to give seasonal information to support the source apportionment. Results of the PCA included 6 factors for Thoyohandou and Pretoria and 7 factors for Cape Town. Where PCA produced strong statistical support to the number of factors chosen, correlations between HYsplit and DN-PMF and seasonal output corroborated the assigning of sources to factors. Utilising two models for factorization during source apportionment limits the error due to subjectivity where one method of PM2.5 sampling was used.

How to cite: Howlett-Downing, C.: Source apportionment for PM2.5 and trace element bound PM2.5 using PMF and PCA at three sites in South Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-645, https://doi.org/10.5194/egusphere-egu24-645, 2024.

X5.88
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EGU24-587
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ECS
Building local capacity for air quality monitoring devices in Africa: Lessons from Nigeria
(withdrawn after no-show)
Dada Joseph and Rabiu Babatunde
X5.89
|
EGU24-1291
Data gap in air quality related health analysis and projections in data scarce regions of the world: Case study of West Africa 
(withdrawn after no-show)
igwe-Steve Ewona and the Environmental Monitoring and Energy Research Group - Africa (EMERGAfri)
X5.90
|
EGU24-296
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ECS
Samuel Ogunjo, Babatunde Rabiu, and Ibiyinka Fuwape

The failure of air pollution mitigation strategies at national levels can be attributed to the disconnect between policy makers and creators of this pollutants. This is not unconnected to lack of data at the smallest level of the society. Sparse air quality data in developing countries have hindered policy implementations for it’s reduction. There is the need to compensate for the sparse data using other sources. In this study, the performance of satellite (WashU) and reanalysis (CAMS) data was evaluated against two low cost sensors – Purple Air and Clarity devices, across several locations in Nigeria. Both models were found to perform fairly well during the wet season but poorly during the dry season. We developed correction factors to improve both satellite and reanalysis data over Nigeria. We further leverage on the corrected data to develop a bottom-up approach to tackle air pollution from the grassroots using a credit reward system. This will make every citizen part of the clean-up process while accelerating holistic and fair transitioning to a clean economy.

How to cite: Ogunjo, S., Rabiu, B., and Fuwape, I.: Leveraging satellite and reanalysis air quality data for bottom-up approach in tackling air pollution in nigeria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-296, https://doi.org/10.5194/egusphere-egu24-296, 2024.

X5.91
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EGU24-1131
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ECS
Rubal Rubal, Anirudha Ambekar, Sarath K. Guttikunda, and Thaseem Thajudeen

Air pollution is one of the leading causes of premature death across the world. To gain valuable insights into ambient fine particulate matter (PM) concentrations, a combination of regulatory monitoring networks, satellite retrievals of air-quality-related substances, and air quality models are typically employed. Studies reveal persistent exceedance of World Health Organization and national standards, particularly in developing nations. It is crucial to recognize that numerous regions in Asia and Africa still need proper monitoring systems to understand the emission sources and concentrations. A major obstacle to better spatiotemporal monitoring is the high cost involved in setting up the monitors.

This research investigates the distribution and proportion of PM1, PM2.5, and PM10 in an educational campus using low-cost sensors (PMS5003, PMS 7003, Winsen ZH 06, SPS 30, Honeywell). A comparative analysis was conducted to evaluate the performance of these sensors against the TSI DRX DustTrak 8533 and calibrated with a beta attenuation monitor (BAM). Additionally, to enhance the accuracy and reliability of LCS measurements, the calibration through various regression and machine-learning (ML) techniques was explored under diverse environmental conditions. In the absence of calibration, the PM2.5 correlation (R2) between LCS and DustTrak is 0.62 to 0.73, indicating a moderate to strong relationship. However, compared to BAM, LCS correlation decreases (0.20 to 0.26), suggesting a weaker association. Utilizing ML with meteorological variables improves R2 values to 0.82 to 0.96 for DustTrak and 0.40 to 0.56 for BAM, with lower mean absolute and root mean square errors. The time-series results demonstrated typical seasonal patterns of winter highs and summer/monsoon lows. We also explored the PM concentrations in the kitchen and common dining facility using a combination of validated low-cost PM sensors (LCS) and DustTrak 8433. It is found that the prolonged cooking durations involved in high-heat cooking methods like stir-frying and deep-frying resulted in a rise in PM2.5, causing a higher exposure to PM. Overall, the findings of the study have provided valuable insights into the dynamics of PM2.5 emissions, the impact of cooking activities, effect of chimney and the importance of ventilation to reduce exposure to PM and implementing mitigation strategies to improve indoor air quality and protect human health.

How to cite: Rubal, R., Ambekar, A., Guttikunda, S. K., and Thajudeen, T.: Air Quality Assessment: Analyzing PM Distribution and Calibrating Low-Cost Sensors for Precise Measurements in Indoor and Outdoor Environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1131, https://doi.org/10.5194/egusphere-egu24-1131, 2024.

X5.92
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EGU24-1020
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ECS
|
Lisseth Milena Cruz-Ruiz, Fernando Fernández-Restrepo, Andrés Yarce-Botero, Valeria Solórzano-Araque, and Olga Lucia Quintero

An evaluation of an ensemble of atmospheric Chemical Transport Models (CTMs) was carried out using WRF-Chemand LOTOS-EUROS over the Aburrá Valley, Colombia, an interesting region for huge density of population and mountainous topography. The models were configured in the same spatiotemporal domain and the pollutants of interest were nitrogen dioxide (NO2) and ozone (O3) due to their significant impact on sensitive ecosystems. The ensemble concentrations were assessed by comparing them to the data collected by local air quality monitoring stations against the simulated surface concentration. Additionally, vertical profiles were compared between each model and the ensemble. The Weather Research and Forecasting (WRF) is utilised as the Numerical Weather Model (NWM) driver in the two CTMs with identical initial and boundary conditions, to let the chemical operators from each model to be the contributors to the differences incorporated with an ensemble. The statistical comparison to assess the ensemble includes various metrics expressed in Taylor diagrams. These metrics comprise the mean factorial bias (MFB), root mean square (RMSE) error, and correlation factor (Corr) to evaluate the models and its ensemble against the measurements. The ensemble perspective of the Chemical Transport Model (CTM) diminishes the drawbacks of each CTM and enables us to comprehend the impact of key dynamics over rough topography that has a direct influence on the vertical transport of pollutants. The findings reveal which model aligns better with surface observations in the Aburra Valley and enable a qualitative identification of the principal dynamics in pollutant transport across this region utilizing two CTMs and their ensemble.

How to cite: Cruz-Ruiz, L. M., Fernández-Restrepo, F., Yarce-Botero, A., Solórzano-Araque, V., and Quintero, O. L.: Ensemble Modeling of Atmospheric Pollutants: A Case Study with WRF-Chem and LOTOS-EUROS in Aburrá Valley, Colombia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1020, https://doi.org/10.5194/egusphere-egu24-1020, 2024.

X5.93
|
EGU24-806
Integrating Data-Driven Techniques for Assessing Urban Air Quality Dynamics
(withdrawn after no-show)
Dario Fajardo Fajardo, Erick Chávez, and Cristhian Narváez
X5.94
|
EGU24-946
Application of size distribution of particulate matter as a forensic tool for source apportionment
(withdrawn)
Aswathy Valsan, Prem Mohan, and Shabna Shekar
X5.95
|
EGU24-560
|
ECS
Modelled Emission Reduction Potentials from the Transport Sector in Nairobi City
(withdrawn after no-show)
John Kennedy Mwangi, George Mwaniki, William Apondo, Ivy Murgor, and Purity Munyambu
X5.96
|
EGU24-7074
Sina Hasheminassab, David J. Diner, Araya Asfaw, Jeffrey Blair, Sagnik Dey, Rebecca Garland, Pratima Gupta, L. Drew Hill, Fahad Imam, Christina Isaxon, Juanette John, Kristy Langerman, Yang Liu, Christian L’Orange, Tesfaye Mamo, Randall V. Martin, Lotta Mayana, Mogesh Naidoo, Christopher Oxford, and Jeremy Sarnat

Exposure to airborne particulate matter (PM) is the leading environmental risk factor globally. People living in low- and middle-income countries (LMICs) are at higher risk due to elevated levels of PM. Although the connection between total mass concentrations of PM and various health outcomes is well-documented, the relative toxicity of specific PM types—mixtures of particles with different sizes, shapes, and chemical compositions—remains poorly understood. To address this gap, the National Aeronautics and Space Administration (NASA) and the Italian Space Agency (Agenzia Spaziale Italiana, ASI) are jointly implementing the Multi-Angle Imager for Aerosols (MAIA) investigation to explore the association between PM types and adverse health outcomes. The MAIA satellite instrument—a multi-angle imaging spectropolarimeter—will collect targeted measurements of column-integrated aerosol optical and microphysical properties, which will be integrated with measurements from a network of ground-based PM monitors and outputs of the WRF-Chem atmospheric model to generate daily maps of near-surface total PM10, total PM2.5, and speciated (sulfate, nitrate, organic carbon, elemental carbon, and dust) PM2.5 mass concentrations at 1 km spatial resolution. The main focus of the MAIA investigation is a selected set of Primary Target Areas (PTAs) covering highly populated metropolitan regions distributed across the US, Europe, the Middle East, Africa, and Asia. Each PTA encompasses a region that is approximately 360 by 480 km. Three of the MAIA PTAs are in LMICs, including Ethiopia (Addis Ababa and vicinity), South Africa (Johannesburg and vicinity), and India (New Delhi and vicinity), where the MAIA project has deployed and is currently operating various types of surface-based PM pollution monitors. Fabrication of the MAIA satellite instrument was completed in October 2022, and its launch into sun-synchronous Earth orbit is anticipated to occur in 2025. This presentation will cover the ground-based PM monitoring component of the MAIA mission and present preliminary results collected thus far in the low- and middle-income PTAs (LMI-PTAs) and compare the observed total and speciated PM levels to those observed in other countries.

Where available, the MAIA project collects data from existing ground-based PM monitoring networks managed by government agencies, research groups, and other sources. In several PTAs, the MAIA project is capitalizing on the existing SPARTAN Surface Particulate Matter Network for PM2.5 speciation and has expanded this network with additional filter samplers; deployed Colorado State University filter samplers to complement PM2.5 speciation networks; and installed AethLabs microAeth MA350 monitors for black carbon measurements. In Ethiopia, where only a few PM2.5 monitors have historically been operating, a set of cost-effective PurpleAir sensors has been deployed to enhance the spatial coverage of ground-based PM2.5 measurements. The preliminary surface monitoring results indicate highly elevated PM concentrations in LMI-PTAs, which regularly exceed the WHO air quality guidelines. Notably, black carbon is found to be exceptionally high in these regions, reaching levels up to twelve times greater than those measured in developed countries.

How to cite: Hasheminassab, S., Diner, D. J., Asfaw, A., Blair, J., Dey, S., Garland, R., Gupta, P., Hill, L. D., Imam, F., Isaxon, C., John, J., Langerman, K., Liu, Y., L’Orange, C., Mamo, T., Martin, R. V., Mayana, L., Naidoo, M., Oxford, C., and Sarnat, J.: Ground-Based Speciated Particulate Matter Monitoring as Part of the Multi-Angle Imager for Aerosols (MAIA) Investigation: A Focus on Low- and Middle-Income Countries , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7074, https://doi.org/10.5194/egusphere-egu24-7074, 2024.

X5.97
|
EGU24-88
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ECS
Raeesa Moolla, Adegun A. Oluwole, Clinton W. Nyathi, and Rebecca Garland and the IGAC-led TOAR Africa over Ozone Working Group

Air pollution and climate change threaten Africa’s development because of their negative impacts on human health, well-being and productivity. Air pollution and climate change reduce agricultural productivity, for example, with implications for food and nutritional security. Two of the biggest issues for African countries are the lack of data on the emissions causing air pollution and climate change, and inadequate policy and implementation capacity. Countries need this data to plan policies that can reduce air pollution and deliver national development priorities and climate goals. Ozone as an anthropogenic greenhouse gas affects the climate beyond increased warming due to its impact on evaporation rates, cloud formation,precipitation levels and atmospheric circulation..  Combustion of fossil fuels is one of the principal processes that release the gaseous precursor pollutants that react to form O3, which is a major factor in Africa, as most populations on the continent are dependent on fossil and  other fuels for heating and cooking,also, emissions from vehicle exhausts and electric generators with unknown ozone concentrations (Ihedike et al, 2023)  . Furthermore, along with the presence of the precursor gases, many meteorological conditions promote the formation of O3 (Guar et al, 2014). The formation, transport, chemical destruction, deposition and atmospheric lifetime of O3 will determine its concentration in any given area.

Aircrafts  and satellites provide a global-scale view on tropospheric ozone and its precursors, with different types of sensors being sensitive to different parts of the atmosphere. They help in improving forecasting of weather conditions and more recently in improving   predictions of air quality. Data from these aircraft and satellite  sensors are complementary to more detailed and more precise data sets . A wide variety of trends and variations in tropospheric ozone were reported by aircraft sensors in the Tropospheric Assessment Report (TOAR Phase I) in the 1990s (Gaudel 2018).The distribution of the tropospheric ozone over the western Pacific Ocean has also been observed during aircraft experiments, but in Africa there has been little information on these data findings and as well their potential limitations. However, this project intends to address these knowledge gaps. Data from these aircraft sensors are complementary to more detailed and more precise in-situ data that are spatially and temporally limited in which this study intends to address. Prelimanary results from the Working Group will be presented, as well as challenges and limitations of data aquisition and data represntation, in data scarce regions.

How to cite: Moolla, R., Oluwole, A. A., Nyathi, C. W., and Garland, R. and the IGAC-led TOAR Africa over Ozone Working Group: The potential of using a combination of in-situ, campaign and flight data to analyse Ozone across data sparse regions in Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-88, https://doi.org/10.5194/egusphere-egu24-88, 2024.

X5.98
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EGU24-19373
Federico Fierli, Dominika Czyzewska, Sara Basart, Rebecca Garland, Cathy Clerbaux, Vincent Gabaglio, and Anu-Maija Sundstrom

EUMETSAT’s (European Organisation for the Exploitation of Meteorological Satellites) contribution to global air quality monitoring is multifaceted, encompassing technological advancements, long-term commitments, and a collaborative approach to address environmental challenges.

The organization has been actively involved in satellite observations since 1990 through programs like Meteosat and Metop, and since 2015, it has been contributing to the Copernicus EU program. This effort is particularly significant for supporting air quality monitoring in developing countries, where reliable in-situ observatories are limited, and there is high vulnerability to pollutants and climate change impacts. The data provision is set to continue for the next two decades thanks to next-generation missions such as Meteosat Third Generation (MTG). These missions, along with instruments like Sentinel-4 and Sentinel-5 under the Copernicus EU program, are dedicated to air quality monitoring in specific regions, including North Africa and globally.

Here, we will showcase how the atmospheric composition data obtained from EUMETSAT's satellites can be utilized for air quality analysis at the continental and local scale. Recent scientific applications based on datasets from Infrared Atmospheric Sounding Interferometer (IASI) and Sentinel instruments will be reviewed. In addition, examples on how EUMETSAT's satellite data is used to monitor phenomena that have direct implications for health and security, such as desert dust storms and wildfire emissions. A critical part of the discussion will focus on the advantages and drawbacks of satellite data due to observational configurations. This may involve addressing challenges and limitations while highlighting the strengths of satellite observations for air quality monitoring. Finally, it will be shown the importance of data access and training for effective utilization of satellite data. Additional value can be derived from satellite information through techniques like data assimilation and the application of artificial intelligence and machine learning (AI-ML methods).

How to cite: Fierli, F., Czyzewska, D., Basart, S., Garland, R., Clerbaux, C., Gabaglio, V., and Sundstrom, A.-M.: Observing air pollution from satellite: EUMETSAT contribution to air quality monitoring at the global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19373, https://doi.org/10.5194/egusphere-egu24-19373, 2024.

X5.99
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EGU24-17403
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ECS
Asha S Viswanathan, Sarath Guttikunda, and Rahul Goel

Low- and middle-income countries (LMICs) often have the highest levels of air pollution. At the same time, there is a serious lack of routinely collected data (e.g., traffic counts) to develop emission inventories and guide evidence-based policy interventions. The spatial resolution of emission inventories by international research groups (e.g., EDGAR) is often too coarse to represent within-city variation. There is an urgent need to identify cost-effective data sources and develop methods that can be readily applied across LMICs to generate emission inventories at high spatiotemporal resolution. We will present a review of potential big data sources, highlight their strengths and limitations, and propose methodological framework for data fusion to develop transport emissions inventory for an LMIC setting (New Delhi, India).

While many transport inventories have been published for this setting in the past, they have limited reproducibility and often depend on data sources that are static in nature (e.g., vehicle registrations) and are annual estimates. The spatial resolution of these inventories is improved using assumed proxies (e.g., type of road), and temporal resolution using traffic count data or surveys. In some cases, the available data is supplemented by data- and time-intensive traffic simulation studies. We propose that these limitations can be overcome by big data sources combined with ground truth using context-specific low-cost observational surveys.

Through our preliminary review, we identified the following typologies of big data sources: a) satellite or aerial imagery, b) street imagery (e.g., google street view), c) ground-based instrumentation (e.g., CCTV), and d) crowd-sourced GPS data trajectories. The satellite/aerial data, with varying image resolutions (as high as 0.1 m) and their update frequency (as frequent as 1 day), are promising in their potential for vehicle detection to estimate a spatial spread of traffic and to detect longitudinal changes. Street imagery can supplement overhead satellite imagery through accurate detection of smaller vehicles (e.g., motorcycles). GPS data can be used for routing of vehicles, and CCTV recordings (at limited number of locations) can provide diurnal variation and accurately identify types of vehicles.

Use of such data has methodological challenges and requires multidisciplinary skills. Big data is analysed using machine learning methods and computer vision techniques, supported by high-performance computing resources. There is also a need to develop data fusion techniques to harmonise and integrate data across different sources (spatially detected vehicles, GPS routing, and time varying vehicle counts). Additional details of vehicle age, fuel type and emission factors are estimated from public datasets and literature. While challenging, this is usually a one-time procedure for a setting, after which revisions do not require the same amount of time or effort. Using New Delhi, India as a case study, the talk will discuss the application of these data sources and methods.

How to cite: Viswanathan, A. S., Guttikunda, S., and Goel, R.: Review of Big Data Sources for High Spatial and Temporal Resolution On-Road Transport Emission Inventories, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17403, https://doi.org/10.5194/egusphere-egu24-17403, 2024.

X5.100
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EGU24-40
Rabia Sa’id S. and Shettima Hussain

This paper analyzes the optical properties and air mass trajectory of aerosol pollution over North-West (NW) Nigeria. The paper studied the Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA) and air mass trajectory analysis of aerosols pollution over NW Nigeria from 2018 to 2022. For this purpose, the use of satellite products from Ozone Monitoring Instrument (OMI), the Moderate Resolution Imaging Spectroradiometer (MODIS), and back trajectories of air movements calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were employed. AOD values were found to reach peak values (~1 at 500 nm) across the years in the study except in 2020, which, showed a steep decline in AOD values during the period. It is surmised that the decline might have been due to the lock down due to Covid19 when vehicular movement that generate particulate matter and black carbon, construction that generate dust plumes were minimal. Analysis of the optical characteristics of the aerosols studied, supported the observation that the pollution consists of mainly Saharan dust and anthropogenic aerosols with a well-defined seasonal cycle. This assumption was confirmed by HYSPLIT backward trajectories and MODIS images.

Keywords: MODIS, Aerosol Optical Depth, Single Scattering Albedo, Angstrom Exponent, Ozone Monitoring Instrument, HYSPLIT.

How to cite: Sa’id S., R. and Hussain, S.: Analysis of Aerosol Pollution over North-Western Nigeria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-40, https://doi.org/10.5194/egusphere-egu24-40, 2024.

X5.101
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EGU24-202
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ECS
Application of air sensors to support clean cooking initiatives at education centres in low- and middle-income countries using Accra High School, Ghana as a case study.
(withdrawn after no-show)
Victor Dzidefo Ablo, Collins Gameli Hodoli, Zanetor Agyeman-Rawlings, Kojo Tsikata, Lord Offei-Darko, Angela Schmitt, Reginald Quansah, Carl Malings, Daniel M Westervelt, and Mohammed Iqbal Mead
X5.102
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EGU24-295
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ECS
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asinta manyele and Mbazingwa Mkiramweni

The ambient air quality of Dar es Salaam city has been monitored using 14 low cost PurpleAir PA-II sensors deployed May 2021 to measure particulate matter (PM2.5 and PM10).  Daily average of PM2.5 and PM10  concentration recorded at sites across Dares Salaam city between May 2021 and  February 2022, has been analyzed. Higher PM2.5 and PM10 concentration values were observed at Pugu Dampo (77.26 μg/m3 and 90.89 μg/m3 μg/m3, respectively) and DMDP Magomeni (77.25 μg/m3 and 94μg/m3 respectively) as compared to other stations. While Pugu Dampo is an open municipal waste dumpsite, DMDP Magomeni station is located near traffic congestion dominated junction part of the city. Comparing the observed daily mean values of PM2.5 and PM10 across the stations for the study period, the higher value was from Pugu Dampo (37.94 μg/m3) and Vingunguti Primary school (47.49 μg/m3) respectively. Vingunguti Primary schools, is located in industrial area part of the Ilala District and also is near the major traffic roads and not far from the international airport.  Overall, it has been observed that most observed daily mean and maximum values of PM2.5 and PM10 from sensor stations near busy traffic roads, at the municipal waste dumpsite and in industrial areas exceeds the recommended WHO values. Also, the analyzed data, reveals that the pollution from particulate matter varies greatly across the municipal stations and with time of the day.

How to cite: manyele, A. and Mkiramweni, M.: Trends of Particulate Matter PM2.5 and PM10 Concentrations in Dar Es Salaam City Between 2021 and 2022 as measured by Low cost Sensors., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-295, https://doi.org/10.5194/egusphere-egu24-295, 2024.

X5.103
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EGU24-230
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ECS
Aerosol Chemical Composition and Sources over East Africa’s Background Atmosphere
(withdrawn)
Leonard Kirago, Örjan Gustafsson, Samuel Gaita, Sophie Haslett, H. Langley DeWitt, Jimmy Gasore, Katherine E. Potter, Ronald G. Prinn, Maheswar Rupakheti, Jean D. Ndikubwiwamana, Bonfils Safari, and August Andersson
X5.104
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EGU24-311
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ECS
Quantifying Transboundary Air Pollution in Bangladesh using Low-Cost Sensors
(withdrawn after no-show)
Shahid Uz Zaman and Abdus Salam
X5.105
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EGU24-836
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ECS
Air Quality Issues Associated with Wildfire Events in the Indian Himalayan Region  
(withdrawn after no-show)
Anandu Prabhakaran and Piyush Srivastava
X5.106
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EGU24-453
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ECS
Abid Omar and Colleen Marciel F. Rosales

Cities in Pakistan consistently report extreme levels of air pollution, with our low-cost sensor data ranking Lahore as the most polluted major city in the world with annual average PM2.5 levels at 105 µg/m3, and hourly peak eposides reaching 900 µg/m3. At the same time, these cities remain a ‘data gap’ in terms of availability of air quality data with sporadic monitoring by government or by other institutions. Moreover, there is little scientific research interest for Pakistani cities, especially in comparison with other regional LMIC cities that have relatively lower air pollution levels and affected populations.

This paper is the first comprehensive survey of available data for Pakistan, by publishing seven years of low-cost sensor data collected by a low-cost sensor community network for the 4 largest cities in Pakistan, namely Karachi, Lahore, Islamabad, and Peshawar. Statistical comparisons with available reference-standard data and remote sensing data is also done.  

Challenges in data collection are also covered when working in LMIC city areas affected by access issues, intermittent electricity supply and data outages, as well as the learnings from seven years of community work, and use of sensors for community awareness and advocacy in Pakistan. The impact from this community monitoring network has been instrumental in kick-starting awareness in one of the most air-polluted regions of the world.

How to cite: Omar, A. and Marciel F. Rosales, C.: Open Air Quality Community Data in Pakistan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-453, https://doi.org/10.5194/egusphere-egu24-453, 2024.

X5.107
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EGU24-498
Forecasting of air pollution episodes using machine learning in Kathmandu Valley
(withdrawn after no-show)
Binod Pokharel
X5.108
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EGU24-843
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ECS
Improving air quality management through forecasts: From Science to Policy to Action 
(withdrawn)
Adeel Khan, Uday Suryanarayanan, Mohammad Rafiuddin, and Jayachandran Kollayil
X5.109
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EGU24-921
Nazneen Nazneen and Aditya Kumar Patra

Particulate matter (PM2.5 and PM10), black carbon (BC), and ultrafine particle (UFP) exposures among toll station workers on a highway in India were measured in both summer and winter seasons. Results showed that toll workers inside the toll collection booths (Tinside) were exposed to higher concentrations of air pollutants than those working outside the booths (Toutside), except for UFP. The concentrations of PM2.5 were higher during winter than summer: 152.3 > 86.1 µg m-3 (Tinside) and 136.6 > 79.2 µg m-3 (Toutside), while PM10 concentrations were 205.8 > 169.5 µg m-3 (Tinside) and 185.3 > 156.4 µg m-3 (Toutside), and BC concentrations were 38.8 > 34.5 µg m-3 (Tinside) and 22.2 > 18.5 µg m-3 (Toutside). In contrast, UFP concentrations were higher at Toutside (31312 > 22000 pt cm-3) than Tinside (21610 > 18000 pt cm-3). The diurnal variation of pollutants showed higher concentrations in the evening hours due to increased traffic, low wind speed and less atmospheric dispersion. Further, a significant correlation was found between pollutants and meteorological parameters (temperature, relative humidity, wind speed, solar radiation and boundary layer height) and traffic volume. Using Multiple-path particle dosimetry model (MPPD), mass deposition in the lungs were determined. Mass deposition found to be higher inside the toll booths workers. The study also revealed that PM particles consisted of soot, mineral and fly ash, which are proxies of fresh exhaust emissions, re-suspended road dust, and industrial emissions, respectively. The presence of Si, Al, Ca and Pb, as confirmed by EDX analyses, indicated the sources of pollutants to be re-suspended road dust, brake/tire wear, and construction dust. The results underscore the importance of implementing policies to control air pollutant levels, especially in workplaces near busy roads.

How to cite: Nazneen, N. and Patra, A. K.: Seasonal Variations in Particle Exposure: A Study at an Indian Highway Toll Station Investigating Coarse, Fine, Ultrafine, and Black Carbon Particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-921, https://doi.org/10.5194/egusphere-egu24-921, 2024.

X5.110
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EGU24-690
Novel approaches to address air pollution using Activity data 
(withdrawn)
Bhavay Sharma, Vandana Tyagi, Ritesh Kumar, Sanjar Ali, Ajay Singh Nagpure, and Prakash Doraiswamy
X5.111
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EGU24-838
Source identification and health risk assessment of atmospheric PM2.5 and PM10 - bound polycyclic aromatic hydrocarbons in Faisalabad, a future megacity in South Asia
(withdrawn after no-show)
Muhammad Ibrahim, Afifa Aslam, Abid Mahmood, and Balal Yousaf
X5.112
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EGU24-967
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ECS
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Mansi Pathak, Vikas Kumar Patel, and Jayanarayanan Kuttippurath

The COVID-19 lockdown (LD) presented a unique opportunity for examining shifts in regional and global air quality. The alterations in atmospheric carbon monoxide (CO) during LD necessitate thorough analysis, given that CO is a significant air pollutant impacting human health, ecosystems, and climate. Our examination reveals a 5-10% decrease in the CO column during LD (April-May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with elevated anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe, and Russia. However, this reduction was absent in regions prone to frequent and intense wildfires and agricultural waste burning (AWB). There is substantial heterogeneity in CO column distributions, ranging from regional to city scales, during the LD period. To identify the sources of CO emissions during LD, we analyzed the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for major cities worldwide. This analysis facilitated the identification of contributions from various sources, including vehicles, industries, and biomass burning during LD. Comparisons between CO levels during LD and PreLD periods indicate a significant reduction in global tropospheric CO but no noteworthy change in the stratosphere. Notably, CO emissions decreased during LD in hotspot regions but rebounded after the lifting of LD restrictions. This study underscores the importance of policy decisions and their implementation on both global and regional scales to enhance air quality, safeguarding public health and the environment.

How to cite: Pathak, M., Patel, V. K., and Kuttippurath, J.: Spatial variability in global atmospheric CO during Covid-19 lockdown: Implication of air quality policies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-967, https://doi.org/10.5194/egusphere-egu24-967, 2024.

X5.113
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EGU24-970
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ECS
Application of measurements and modeling tools for managing air quality at a hyperlocal scale in Indian cities
(withdrawn)
Dheeraj Alshetty, Swagata Dey, Rishabh Dev, Satish Chandra, and Sreekanth Vakacherla
X5.114
|
EGU24-20598
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ECS
Manuj Sharma and Suresh Jain

Air quality degradation in Tier 2 non-attainment Indian cities is a rising concern as the air pollution level is alarmingly increasing, similar to metropolitan cities. Efficient urban air quality management measures in Tier 2 non-attainment cities require a comprehensive emission inventory to support the air pollution abatement strategies. The present study developed a detailed emission inventory of the primary criteria pollutants (PM10, PM2.5, SO2, NOx, CO) and NMVOCs accounting for all the urban anthropogenic sources. The bottom-up emission estimate technique was applied to the Vijayawada metropolitan region, one of India's most polluted tier 2 cities. The emission load of PM10, PM2.5, SO2, NOx, CO, and NMVOCs over Vijayawada were estimated to be 5.65 Gg, 2.13 Gg, 1.38 Gg, 8.91 Gg, 17.17 Gg, and 2.33 Gg respectively in 2021. Road and construction dust accounted for 74% of PM10 and 45% of PM2.5, whereas active motorized vehicular activities significantly contributed to CO (78%) and NOx emissions (69%), and the industrial sector accounted for 96% of the overall SO2 emission load across Vijayawada.

Further, various strategies were tested to project the emission load of PM10, PM2.5, SO2, and NOx under the Business as Usual (BAU) and Alternative (ALT) scenarios for 2025 and 2030, respectively, to evaluate the increased and reduced emission potential. Under the BAU scenarios, the key factors for future projections of sectoral activity data are the population and economic growth of Vijayawada. The BAU scenarios indicated an overall emission increase of 12% and 36% in PM10, 4% and 22% in PM2.5, and 5% and 11% in SO2, whereas the overall NOx emissions are estimated to be reduced by 13% and 14% in 2025 and 2030 respectively. Introducing the BSVI emission standards and phasing out older vehicles were the key scenarios for reducing NOx emissions under BAU scenarios. The strategies tested under the ALT scenarios considered the multi-sectoral control actions declared or proposed by national and state governments owing to the potential to reduce air pollution in the study area. Advanced control techniques, coupled with shifting towards cleaner fuels, may show the potential to reduce the overall PM10 emissions by 13% and 15%, PM2.5 by 16% and 23%, SO2 by 24% and 48%, and NOx emission by 20% and 32% in 2025 and 2030 respectively, under ALT scenarios. The study findings provide a comprehensive emission database encompassing pollutants, sources, and area-specific quantitative information for Tier 2 non-attainment cities. The research outcome will also be used as the guiding tool by the scientific community, air quality researchers, and policymakers for designing practical and feasible air pollution abatement strategies and management plans to provide clean air for present and future generations at the city level.

Keywords: Emission Inventory, Scenarios development, Urban anthropogenic sources, Mitigation measures, Non-attainment cities

How to cite: Sharma, M. and Jain, S.: Bottom-up approach to estimate the present and future air emissions under different policy scenarios in Tier-2 non-attainment city in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20598, https://doi.org/10.5194/egusphere-egu24-20598, 2024.

Posters virtual: Tue, 16 Apr, 14:00–15:45 | vHall X5

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 18:00
Chairpersons: Aderiana Mbandi, Rebecca Garland, Nestor Rojas
vX5.8
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EGU24-573
|
Jill Manapat and John Richard Hizon

Air quality monitoring (AQM) research is still in its nascent stages in the Philippines. One of the few academic research programs in the country focusing on the development of AQM technologies is the Center for Air Research in Urban Environments (CARE). Based at the University of the Philippines Diliman, CARE is an interdisciplinary group funded by the Department of Science and Technology (DOST) that aims to expand the current coverage of AQM stations by complementing a network of affordable AQM nodes. Its technologies include a multi-stakeholder IoT Platform with data processing and modeling capabilities, stationary indoor and outdoor (I/O) sensor nodes that detect a wide array of I/O particulates and gases, an AI-powered eTraffic system, locally designed equivalent black carbon (eBC) and volatile organic compound (VOC) sensors, and mobile and wearable sensors, among others. However, CARE acknowledges the gap between developing AQM technologies in the lab and actual adoption and utilization by its target users. Research and innovation can only achieve its intended impact if they are successfully transferred to society.

This study will explore various translational methodologies implemented by CARE to increase the chances of AQM technology adoption beyond its three-year DOST grant. Strategies in multi-stakeholder engagement (both local and global), participatory design and co-creation, capacity-building, community-building, and other relevant approaches will be examined through case studies and success stories. Lessons learned and implications for future innovations will also be discussed to provide insights for other research groups that are also working towards effective and efficient AQM technology transfer.

How to cite: Manapat, J. and Hizon, J. R.: Airvolution: Translating Emerging Techniques in Air Quality Monitoring to Philippine Society, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-573, https://doi.org/10.5194/egusphere-egu24-573, 2024.

vX5.9
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EGU24-166
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ECS
|
Udita Gupta, Sruthi Jayaraj, and Shiva Nagendra S. M.

Petroleum refineries generate hydrocarbons, SO2, SO3, VOCs, NOx, CO, and PM10/2.5 as air pollutants through fuel combustion for operating process units/stacks, evaporation from tank farms, fugitive emissions, etc., which have been found to be responsible for causing episodic health effects in the population residing nearby the refinery. The stack emissions are one of the lesser studied and a major contributor to air pollution from a petroleum refinery. This paper calculates its emission inventory using the USEPA Methodology based on fuel consumption and simulates ground-level concentration using the dispersion model. Further, the study calculates critical assimilative carrying capacity (ACC) and remaining carrying capacity (RCC) by conducting iterative simulations on CALPUFF View, a Lagrangian approach based Gaussian Puff Dispersion Model. The total emissions of SO2, NO2, PM10, and CO are found to be 2331.57, 1665.34, 213.565, and 800.841 tons/year respectively, the majority of which are contributed by primary and captive process units. The 24-hour average maximum predicted concentration values for SO2 and NO2 are 26.8 μg/m3 and 27.9 μg/m3 respectively which occurred in the winter season. The lower ground levelconcentrations are attributed to the use of fuel oil with sulphur content (0.75% by mass), Sulphur Recovery Unit (SRU) which recovers sulphur element from acid gases and low NOx burners. By iterative simulations, it is found that for SO2 and NO2, 65% of the carrying capacity remains at current emissions and the current RCC of NO2 and SO2 stands at 4327.6 tons/year and 3091.06 tons/year respectively. The minimum RCC is observed for winters, corresponding to the minimum ventilation coefficient of 1517.15 m2/s, and similarly, the maximum RCC is observed for the summer season at maximum ventilation coefficient of 3413.49 m2/s. The peak values of ACC and RCC can be used for planning possible expansion of the refinery and seasonal variation of the ACC and RCC can be used to bring down the plant capacity in winter and post-monsoon seasons.

How to cite: Gupta, U., Jayaraj, S., and Nagendra S. M., S.: Emission Inventory and Critical Assimilative Carrying Capacity of Petroleum Refinery in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-166, https://doi.org/10.5194/egusphere-egu24-166, 2024.

vX5.10
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EGU24-813
George K Varghese, Anson Regi, Prem Mohan, and Jijo Ponnachan

Chemical speciation of ambient particulate matter (PM) is important for understanding the health effects of this most ubiquitous air pollutant. Many studies in India have reported significant contribution of road dust to ambient PM, in some cases as high as 70%. This study presents a quick and affordable method for knowing the approximate chemical composition of the atmospheric PM from the chemical analysis of sediment deposits from ground surface. Sediment from street surface was collected and segregated into different size fractions using sieves. Chemical characterization was done separately for each size fraction, and from the results the composition of dust particles of size less than 10µm was obtained. Ambient PM was monitored using MiniLAS, which is a real time PM monitoring instrument that measures PM in 24 different size channels. Using size as criteria, the fraction of road dust in ambient PM was determined. Now, combining the results of ambient PM monitoring and chemical characterization of street dust, the chemical composition of atmospheric PM was calculated. To confirm the results, PM collected in a high-volume air sampler deployed at the place was analyzed for chemical composition. The results matched with the calculated composition indicating the possibility of adopting this method for environments polluted with resuspended dust, which is very often the case in developing countries.

How to cite: Varghese, G. K., Regi, A., Mohan, P., and Ponnachan, J.: Low cost, rapid method for ambient particulate matter speciation in specific environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-813, https://doi.org/10.5194/egusphere-egu24-813, 2024.

vX5.11
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EGU24-869
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ECS
Assessing the impact of behavioural interventions in reducing air pollution: A meta-analysis   
(withdrawn)
Uday Suryanarayanan
vX5.12
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EGU24-1213
Katja Dzepina, Vaios Moschos, Anna Tobler, Deepika Bhattu, Kaspar Daellenbach, Michael Bauer, Peeyush Khare, Jasna Huremović, Almir Bijedić, Gordana Pehnec, Anne Kasper-Giebl, Sanja Frka Milosavljević, Jean-Luc Jaffrezo, Gaelle Uzu, Dragana Đorđević, Asta Gregorič, Leah Williams, Sarath Guttikunda, Griša Močnik, and Andre Prevot and the SAFICA 2017-2018 and SAAERO 2022-2023 team and collaborators

Particularly during the cold weather season, countries of the Southeast Europe are experiencing some of the poorest air quality in the world due to the extensive use of solid fuels and old vehicle fleets. The city of Sarajevo is the capital of Bosnia and Herzegovina (BiH) situated within a basin surrounded by mountains. In the winter months (domestic heating season), topography and meteorology cause the pollutants to be trapped within the city basin. Countries of the Southeast Europe lack state-of-the-art atmospheric sciences research and access to sophisticated instrumentation and methodology, despite high levels of ambient pollution and position within the European Union (EU) borders, making it imperative to understand the emission sources, processing and the adverse health effects of atmospheric aerosol pollution.

               This presentation will highlight the field measurements in Central and Southeast Europe during the Sarajevo Canton Winter Field Campaign 2017-2018 (SAFICA) and Sarajevo Aerosol Experiment 2022-2023 (SAAERO) projects, centered at the Sarajevo Bjelave supersite. Both projects were envisioned to produce crucial, not previously available information about aerosol emission sources and atmospheric transformations through a combination of online field and offline laboratory measurements. Online measurements during a) SAFICA and b) SAAERO included, a) black carbon, particle number and size distribution, and b) carbonaceous species, elemental composition and bulk chemical composition. SAAERO online measurements also included stationary and mobile measurements of gas- and particle-phase species on board the mobile laboratory in Sarajevo and Zenica, BiH, as well as in Ljubljana, Slovenia and Zagreb, Croatia. Finally, extended SAAERO project included measurements of black carbon at three additional urban centers: Ljubljana, Zagreb, and Belgrade, Serbia, enabling the first comparison of urban air quality in Central and Southeast Europe between two EU and two non-EU capitals.

During both projects, laboratory aerosol analyses determined aerosol bulk chemical composition, selected elements (Huremović et al., 2020; Žero et al., 2022) and molecular species (Pehnec et al., 2020). Aerosol chemical composition determined by aerosol mass spectrometry was further analyzed by Positive Matrix Factorization to separate organic aerosol into subtypes characteristic of specific sources and atmospheric processes. Aerosol oxidative potential was also determined to evaluate aerosol ability to generate reactive oxygen species. Sarajevo and Belgrade have high ambient loadings of aerosol and black carbon, indicative of strong and diverse combustion sources and a major public health hazard. Finally, aerosol surface concentrations will be discussed in the context of European air quality.

We thank Jasminka Džepina, Magee Scientific/Aerosol, TSI and Aerodyne for support. We acknowledge the contribution of the COST Action CA16109 COLOSSAL and SEE Change Net. KDž and ASHP acknowledge the grant by the Swiss NSF (Scientific Exchanges IZSEZ0_189495), KDž, GM and ASHP European Commission SAAERO grant (EU H2020 MSCA-IF 2020 #101028909), GM Slovenian ARIS grant (P1-0385), SF Croatian HRZZ grant (BiREADI IP-2018-01-3105), and AG, MR, MI, BA and IBJ Slovenian ARIS grant (L1-4386).

Pehnec, G., et al., Sci. Tot. Environ., 734, 139414, 2020.

Huremović, J., et al., Air Qual. Atmos. Health, 13, 965–976, 2020.

Žero, S., Žužul, S., et al., Environ. Sci. Technol., 56, 7052−7062, 2022.

How to cite: Dzepina, K., Moschos, V., Tobler, A., Bhattu, D., Daellenbach, K., Bauer, M., Khare, P., Huremović, J., Bijedić, A., Pehnec, G., Kasper-Giebl, A., Frka Milosavljević, S., Jaffrezo, J.-L., Uzu, G., Đorđević, D., Gregorič, A., Williams, L., Guttikunda, S., Močnik, G., and Prevot, A. and the SAFICA 2017-2018 and SAAERO 2022-2023 team and collaborators: Particulate air pollution in the heart of the European Union: lessons learned from SAFICA 2017-2018 and SAAERO 2022-2023 projects in Central and Southeast Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1213, https://doi.org/10.5194/egusphere-egu24-1213, 2024.

vX5.13
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EGU24-1282
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ECS
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Sai Amritha Kuttippurath

Air pollution presents a formidable global challenge, exerting profound impacts on climate, human well-being and the world economy. Notably, atmospheric NO2, predominantly of anthropogenic origin, arises from diverse sources, including vehicular and industrial emissions. This study investigates the changes in global atmospheric NO2 through analysis of satellite and ground-based data, focusing on the period from 2002 to 2019. Elevated NO2 levels (> 8 × 1015 molec./cm2) are identified in regions such as the USA, Europe, India, China, the Middle East (MDE), South Africa (SA), Central Africa (CA) and selected regions in Brazil. Seasonal variability is evident, with peak concentrations in winter and troughs in summer, largely influenced by meteorological conditions and biomass burning. While NOx emissions from road transport dominate the USA and Europe, various industrial activities drive elevated NO2 levels in East China (EC), the Indo-Gangetic Plain (IGP) and SA. Noteworthy is the substantial decline (approximately -0.1 × 1015 molec./cm2/year) in NO2 observed in the USA and Europe during the study period. In contrast, significant positive trends (approximately 0.06–0.1 × 1015 molec./cm22/year) are noted in the MDE, EC, SA, CA and IGP. An additional analysis of NO₂ pollution in 3000 global cities reveals a declining trend in most cities in the USA and Western Europe (WE) at -0.1 × 1015 molec./cm2/year. Conversely, cities in India, China, Africa, Southeast Asia, MDE, and South America exhibit positive trends in NO2, ranging from 0.04 to 0.1 × 1015 molec./cm2/year. The decreasing NO2 trends in developed nations of North America and Europe are attributed to the enforcement of stringent vehicular norms, resulting in a significant reduction in road transport emissions. This study offers a comprehensive overview of recent NO2 pollution trends across nations and cities, highlighting the contrasting trajectories between developed and developing regions. It also suggests potential strategies for developing nations to mitigate air pollution.

How to cite: Kuttippurath, S. A.: Monitoring long-term changes in NO2 pollution from global to city scale: A journey guided by environmental laws and policies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1282, https://doi.org/10.5194/egusphere-egu24-1282, 2024.

vX5.14
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EGU24-8285
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ECS
Ritika Kapoor, Vijay Limaye, and Abhiyant Tiwari

Outdoor air pollution contributed to an estimated 980,000 deaths in 2019 and imposes an enormous public health burden in India. For many Indian cities, average annual exposures are above the current National Ambient Air Quality Standard (annual average of 40 µg/m3). To provide a roadmap for addressing unhealthy air pollution levels, the Government of India launched the National Clean Air Programme (NCAP) in 2019. The NCAP effort aims to reduce fine particulate matter PM2.5 levels 20–30% by 2024 relative to 2017 levels in 132 cities that are in nonattainment for the annual NAAQS. Ahmedabad,  a city of about 8.5 million people in Gujarat state, in 2017 launched continuous air quality monitoring and has been taking steps to reduce PM2.5 levels through NCAP actions.  We investigated publicly available air quality data for Ahmedabad to evaluate how city air quality has changed during NCAP implementation and deploy an air quality and health impact assessment model to estimate the health effects of city air pollution.

Specifically, we configured the Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) with local air pollution, population, and baseline health data to estimate citywide air quality effects on human health from PM2.5 exposures between 2018 and 2022 using air quality exposure-response functions derived from international epidemiological cohort studies. Overall, we find that the average air quality improved slightly in Ahmedabad during the evaluation period, from 63.4 to 56.2 µg/m3 in 2022, based on available air monitoring data. That decrease represents a 7.2% reduction in annual PM2.5 levels, while attainment of the 30% NCAP goal by 2022 would have resulted in an annual PM2.5 level of 44.4 µg/m3 in 2022. Our health effect analysis in BenMAP-CE are estimates changes in annual all-cause mortality by analyzing population-weighted PM2.5 exposures. We estimate that observed reductions in PM2.5 levels from 2018-2022 are associated with 1631 (95% CI, 1234 - 2010) fewer deaths citywide. However, if the 30% NCAP target had been achieved by 2022, the city would have seen 3931 (95% CI, 2984 - 4834) fewer deaths from PM2.5 exposures. Our integrated air quality and health assessment provides a blueprint for other Indian cities to evaluate how air quality changes affecting human health and shows how a focus on the health impacts of cleaner air can support future NCAP implementation efforts nationwide.

 

How to cite: Kapoor, R., Limaye, V., and Tiwari, A.: Estimating The Health Benefits from Air Quality Improvements in Ahmedabad, India Under the National Clean Air Programme, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8285, https://doi.org/10.5194/egusphere-egu24-8285, 2024.