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.

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.

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 PM_2.5, 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 PM_2.5, 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 (PM_2.5), 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 PM_2.5 (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.

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.

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. PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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; PM_2.5; 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.

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.

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 (PM_2.5) and black carbon (BC), investigate source contributors to air pollution using source apportionment analysis, and produce spatiotemporal land use regression (LUR) models to estimate PM_2.5 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 PM_2.5 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 PM_2.5 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.

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.

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.

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.

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.

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 (PM_2.5), 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.

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.

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 PM_2.5 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 PM_2.5 quartz filter samples were collected in Nairobi city and analyzed for mass concentration and PAHs (known for their carcinogenic and mutagenic properties). The average PM_2.5 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.

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.

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.

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.

Fine particulate matter, PM_2.5 - 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 PM_2.5 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 PM_2.5 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.

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 𝐺^∗𝑖) 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.

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.

The PM_2.5 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 PM_2.5 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 PM­_2.5 pollution. The RF model accurately captured the variation of PM_2.5 (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 PM_2.5 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 PM_2.5 concentrations. The results suggest that controlling concentrations of secondary aerosols below 25 μg/m³ and biomass burning below 15 μg/m³can reduce the overall PM_2.5 concentration by over 2.5 times. It is to be noted that even after the strategic control measures, PM_2.5 concentrations are predicted to be over 100 μg/m³. Given the critical role of secondary aerosols in PM_2.5 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.

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.

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 PM_2.5 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.

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 (PM_2.5) - 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5. 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 PM_2.5 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, NO_x, 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 PM_2.5 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 NO_x 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 PM_2.5 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.

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 (PM_2.5) leading to a decline in air quality and negative health impacts within densely populated coal mining regions. PM_2.5-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

PM_2.5 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 PM_2.5 exposure levels (~500 ± 190 µg/m³) were observed near coal-haul road than at the residential sites. PM_2.5 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 OP_vol^AA and OP_vol^DTT—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 (OP^AA) and 0.9 ± 0.12 nmol min-1 m-3 (OP^DTT). The inhalation risk for PM_2.5 was observed to be 4 x 10^-4 indicating a significant risk to the population. The study highlights the high exposure to PM_2.5 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.

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 (PM_2.5), 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.

The Greater Kuala Lumpur (GKL) is the most urbanised region in Malaysia that experiences persistently high levels of PM_2.5. Transboundary haze caused by biomass burnings in Sumatra, Indonesia, exacerbates the PM_2.5 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 PM_2.5 concentration levels in GKL during the transboundary haze of September 2019. Our research addresses four key objectives: i)analysing PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 levels. We utilised the Global Fire Assimilation System (GFASv1.2) biomass emission inventory to estimate the total PM_2.5 mass emitted from biomass burning events within our domain, latitude 5^oS to 10^oN and longitude 95^oE to 110^oE. Our estimation showed that 1.78 teragrams (Tg) of PM_2.5 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 PM_2.5 mass emissions from Sumatran biomass burnings and elevated PM_2.5 concentration levels in GKL in September 2019. Emissions from Jambi, Riau and South Sumatra provinces accounted for approximately 94% of the total PM_2.5 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 PM_2.5 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  PM_2.5 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  PM_2.5 concentration levels in GKL during transboundary haze. Of these, fires from Riau dominated PM_2.5 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 PM_2.5 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.

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 PM_2.5 variations throughout the country. Limited PM_2.5 data has led previous health studies to rely on publicly-available monthly PM_2.5 estimates. However, these estimates have large uncertainties over the under-monitored regions, including India because PM_2.5 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 PM_2.5. To bridge these gaps, it is imperative to develop daily PM_2.5 datasets with robust spatial and temporal certainty.

This study develops open-source daily PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 dataset, with an R² of 0.74 and within R² of 0.52. 

Utilizing our PM_2.5 predictions, we identified that 31% of 10 km grid cells across the country demonstrated a more than 5% reduction in PM_2.5 concentrations in 2018-2022 compared to 2005–2010, and any decrease in PM_2.5 was observed in 75% of the locations. Additionally, population-weighted annual average PM_2.5 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 PM_2.5 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 PM_2.5 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.

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.