With the increasing attempt to empower citizens and civil society in promoting virtuous behaviours and relevant climate actions, novel user-friendly and low-cost tools and sensors are nowadays being developed and distributed on the market. Most of these sensors are typically easy to install with a ready-to-use system, while measured data are automatically uploaded on a mobile application or a web dashboard which also guarantees secure and open access to measurements gathered by other users. However, the quality of the datum and the calibration of these sensors are often ensured against research-grade instrumentations only in the laboratory and rarely in real-world measurement. The discrepancies arising between these low-cost sensors and research-grade instrumentations are such that the first might be impossible to use if a validation (and re-calibration if needed) under environmental conditions is not performed. Here we propose a validation procedure applied to the MeteoTracker, a recently developed portable sensor to monitor atmospheric quantities on the move. The ultimate scope is to develop and implement a general procedure to test and validate the quality of the MeteoTracker data to compile user guidelines tailored for on-the-move sensors. The result will evaluate the feasibility of MeteoTracker (and potentially other on-the-move sensors) to integrate the existing monitoring networks on the territory, improve the atmospheric data local coverage and support the informed decision by the authorities. The procedure will include multi-sensor testing of all the sensor functionalities, validation of all data simultaneously acquired by several sensors under similar conditions, methods and applications of comparisons with research-grade instruments. The first usage of the MeteoTracker will be also presented for different geographical contexts where the sensors will be used for citizen science activities and develop a monitoring network of selected Essential Variables within the HORIZON-EU project I-CHANGE (Individual Change of HAbits Needed for Green European transition).

How to cite: Barbano, F., Brattich, E., Cintolesi, C., Iurato, J., Mazzarella, V., Milelli, M., Nizamani, A. G., Sarfraz, M., Parodi, A., and Di Sabatino, S.: Developing and testing a validation procedure to successfully use on-the-move sensors in urban environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14459, https://doi.org/10.5194/egusphere-egu23-14459, 2023.

EMER-Met is the new meteorological forecasting system for the protection of the population in Switzerland. It provides the meteorological basis for coping with all types of emergencies, especially in case of nuclear and chemical accidents. EMER-Met consists of a dedicated upper air measurement network and a high-resolution numerical weather prediction model. The measurement network is composed of state-of-the-art remote sensing instruments to measure accurate wind and temperature profiles in the boundary layer. At three sites, a radar wind profiler PCL1300, a Doppler lidar Windcube-200s and a microwave radiometer Hatpro-G5 are installed. The data from the measurement network are assimilated into the operational 1-km ensemble numerical weather prediction (NWP) system. In the case of the microwave radiometers, we assimilate the brightness temperatures using an adapted version of the RTTOV observation operator. To ensure best impact on the NWP results, the data quality of the measurements is of high importance and is monitored closely on a daily and monthly basis against radiosondes and the NWP model itself. EMER-Met is operational since 2022 and to our best knowledge, it is the first time that the brightness temperatures measured by surface-based microwave radiometers are assimilated operationally. This presentation will focus on the upper air network performance and its impact on NWP. 

How to cite: Haefele, A., Hervo, M., Bättig, P., Leuenberger, D., Merker, C., Regenass, D., Kaufmann, P., and Arpagaus, M.: An integrated meteorological forecasting system for emergency response, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16779, https://doi.org/10.5194/egusphere-egu23-16779, 2023.

Monitoring networks, able to effectively provide high-frequency geochemical data for characterizing the geochemical behavior of the main greenhouse gases (i.e., CO₂ and CH₄) and pollutants (e.g., heavy metals) are crucial tools for the assessment of air quality and its role in climate changes. However, the provision of measurement stations dedicated to monitor gas species and particulate in polluted areas is complicated by the high cost of their set-up and maintenance. In the last decade, traditional instruments have tentatively been coupled with low-cost sensors for improving spatial coverage and temporal resolution of air quality surveys. The main concerns of this new approach regard the in-field accuracy of the low-cost sensors, being significantly dependent on: (i) cross-sensitivities to other atmospheric pollutants, (ii) environmental parameters (e.g., relative humidity and temperature), and (iii) detector signal degradation over time.

This study presents the results of a geochemical survey carried out in the Greve River Basin (Chianti territory, Central Italy) from May to September 2022 by adopting two measuring strategies: (i) deployment of a mobile station, along predefined transepts within the Greve valley, equipped with a Picarro G2201-i analyzer to measure CO₂ and CH₄ concentrations and δ¹³C-CO₂ and δ¹³C-CH₄ values (‰ vs. V-PDB) by Wavelength-Scanned Cavity Ring-Down Spectroscopy (WS-CRDS); (ii) continuous monitoring, at five fixed sites positioned at different altitudes, of CO₂ and CH₄ concentrations through prototyped low-cost stations, coupled with atmospheric deposition and rain samplers to collect particulate samples for chemical lab analysis. The low-cost monitoring stations housed (i) a non-dispersive infrared (NDIR) sensor for CO₂ concentrations, (ii) a solid-state metal oxide sensor (MOS) for CH₄ concentrations, (iii) a laser light scattering sensor (LSPs) for PM_2.5 and PM₁₀ concentrations, and (iv) a sensor for temperature and relative humidity in the air. The CO₂ and CH₄ sensors have been calibrated in-field based on parallel measurements with the Picarro G2201-i and elaborating the calibration data with the Random Forest machine learning-based algorithm.

The measurements carried out along the transepts showed that the downstream areas next to the metropolitan city of Florence were affected by the highest concentrations of CO₂ and CH₄, marked by isotopic signatures revealing a clear anthropogenic origin, mainly ascribed to vehicular traffic. The distribution of these carbon species reflected the evolution of the atmospheric boundary layer, displaying higher concentrations during the early morning, when gas accumulation occurred due to stable atmospheric conditions, and lower concentrations during daytime when the heating of the surface favored the dilution of air pollutants due to the establishment of convective turbulence. These observations were confirmed by the network of low-cost stations, which allowed to simultaneously monitor the distribution of the atmospheric pollutants at different altitudes in the valley. The distribution of particulate was consistent with that of the gaseous species, and the main sources were clearly distinguished based on the chemical composition of the atmospheric deposition in the collection sites. The promising results from the present study could result in an affordable approach to effectively improve air quality monitoring strategies and support data-driven policy actions to reduce carbon emissions.

How to cite: Biagi, R., Ferrari, M., Tassi, F., and Venturi, S.: Multi-instrumental approach for air quality monitoring: characterization and distribution of greenhouse gases and atmospheric metal deposition in the Greve River Basin (Chianti territory, Central Italy)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11385, https://doi.org/10.5194/egusphere-egu23-11385, 2023.

Emissions of nitrogen compounds, including nitrogen dioxide (NO₂) and ammonia (NH₃), have significant impacts on air quality and the environment. To effectively monitor the spatial and temporal variability of these emissions and the efficacy of emission mitigation measures, OnePlanet Research Center is developing a low-cost sensor system to monitor outdoor NO₂ and NH₃concentrations. This sensor system is designed to be deployable in fine-grained networks to accurately capture the dispersion from an emitting source. The deployment of multitudes of such sensor systems will result in large volumes of data. For this purpose, we developed a data infrastructure using the OGC SensorThings API and TimescaleDB, a time-series database extending PostgreSQL. This infrastructure allows for the efficient storage, management, and analysis of large volumes of spatiotemporal data from various sources, such as air quality monitoring networks, meteorological data, and agricultural practices. We demonstrate the potential of this infrastructure by using it in citizen science project COMPAIR, combining data from various sensors to gain insights on the air quality impact of urban circulation policies. The resulting data platform will facilitate the development of decision support tools and the implementation of targeted emission reduction strategies.

How to cite: Bertocci, D., Celikkol, B., Zhuang, S., and Fabius, J.: Data infrastructure for nitrogen compound emissions monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7462, https://doi.org/10.5194/egusphere-egu23-7462, 2023.

How to cite: Salameh, T., Tison, E., Stratigou, E., Dusanter, S., Gaudion, V., Jamar, M., Tillmann, R., Rohrer, F., Winter, B., Verea, T., Muñoz, A., Bachelier, F., Daele, V., and Grandjean, A.: ACTRIS - CiGas side-by-side interlaboratory comparison of new and classical techniques for formaldehyde measurement in the nmol/mol range, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17535, https://doi.org/10.5194/egusphere-egu23-17535, 2023.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes,  but obtaining high quality data can be costly, with each site in a monitoring network requiring instrumentation and skilled operator time. It is therefore important to ensure that each monitor in the network is providing unique data to maximize the value of the entire network.  Differences in measurement approaches for the same chemical between monitoring stations may also result in discontinuities in the network data.  Both of these factors suggest the need for objective, machine-learning methodologies for monitoring network data analysis.   

Air quality models are another valuable tool to augment monitoring networks.  The models simulate air quality over a large region where monitoring may be sparse. The gridded output from air-quality models thus contain inherent information on the similarity of sources, chemical oxidation pathways and removal processes for chemicals of interest, provided appropriate tools are available to identify these similarities on a gridded basis.  The output from these models can be immense, again requiring the use of special, highly optimized tools for post-processing analysis.

Spatiotemporal clustering is a family of techniques that have seen widespread use in air quality, whereby time-series taken at different locations are grouped based on the level of similarity between time-series data within the dataset.   Hierarchical clustering is one such algorithm, which has the advantage of not requiring an a priori assumption about how many clusters there might be (unlike K-means).  However, traditional approaches for hierarchical clustering become computationally expensive as the number of time-series increases in size, resulting in prohibitive computational costs  when the total number of time-series to be compared rises above 30,000, even on a supercomputer.  Similarly, the comparison and clustering of large numbers of discrete data (such as multiple mass spectrometer data sampled at high time resolution from a moving laboratory platform) becomes computationally prohibitive using conventional methods. 

In this study we present a high-performance hierarchical clustering algorithm which is able to run in parallel over many nodes on massively parallel computer systems, thus allowing for efficient clustering for very large monitoring network and model output datasets.   The new high-performance program is able to cluster 290,000 annual time series (from either monitoring network data or gridded model output) in 13 hours on 800 nodes. We present here some example results showing how the algorithm can be used to analyse very large datasets, providing new insights into “airsheds” depicting regions of similar chemical origin and history, different spatial regimes for nitrogen, sulphur, and base cation deposition, .  These analyses show how different processes control each species at different potential monitoring site locations, via cluster-generated airshed maps for each species. The efficiency and flexibility of the algorithm allows for extremely large datasets to be analysed in hours of wall-clock time instead of weeks or months. The new algorithm is being used as the numerical engine for a new tool for the analysis of EU monitoring network data. 

How to cite: Lee, C., Makar, P., and Soares, J.: Spatio-temporal clustering on a high-performance computing platform for high-resolution monitoring network analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8841, https://doi.org/10.5194/egusphere-egu23-8841, 2023.

22 cost-efficient (aka ‘low-cost’) commercially available particulate matter (PM) measurement devices were installed in a diverse urban area in Leipzig, Germany. The instruments measure mostly PM2.5, some additionally PM10, and are equipped with methods for quality assurance such as conditioning to a defined temperature and regular internal calibration. In order to investigate the spread between the instruments and to enable a pre-campaign calibration, all instruments were setup in the laboratory and the outside air and compared against the same reference measurements.

Since July 2022, the measurement network was installed. It covers roughly 2x2 km² and holds different urban features like residential and commercial buildings, important main roads, city parks, and small open building gaps. Within the network there is an official air quality monitoring station located directly at a main road. In addition, at two further official monitoring stations as well as at observation stations of the Leibniz Institute for Tropospheric Research instruments were installed to study the long-term performance, dependence on meteorological conditions and comparison to reference measurements. The measurements will take place until end of 2023.

The cost-efficient instruments perform generally quite well after the calibration. In particularly for higher PM loads > 10 µg m^-3 the agreement against references is mostly satisfying. However, under very high relative humidity and cold temperatures, some instruments lacked to condition the air sufficiently. Despite these difficulties, the chosen instruments have the potential for application in monitoring of air quality limit values, i.e. the answer the question how often are certain limits exceeded.

Furthermore, differences between different local features in the observation area could be observed in e.g., the diurnal cycle but also peak and mean concentrations.

This work is co-financed with tax funds on the basis of the budget passed by the Saxon State Parliament (funding number 100582357).

How to cite: Schrödner, R., Alas, H., and Voigtländer, J.: Application of cost-efficient particulate matter measurement devices in an urban network and comparison to state-of-the-art air quality monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9356, https://doi.org/10.5194/egusphere-egu23-9356, 2023.

The Global Atmosphere Watch (GAW) Programme was established in 1989 in recognition of the need for improved scientific understanding of the increasing influence of human activities on atmospheric composition and subsequent societal impacts. It is implemented as an activity of the World Meteorological Organization, a specialized agency of the United Nations system, and is funded by the organization member countries.

As an international programme, GAW supports a broad spectrum of applications from atmospheric composition-related services to contribution to environmental policy. The examples of the later include provision of a comprehensive set of high quality and long-term globally harmonized observations and analysis of atmospheric composition for the United Nations Framework Convention on Climate Change (UNFCCC), the Montreal Protocol on Substances that Deplete the Ozone Layer and follow-up amendments, and the Convention on Long-Range Transboundary Air Pollution (CLRTAP).

The programme includes six focal areas: Greenhouse Gases, Ozone, Aerosols, Reactive Gases, Total Atmospheric Deposition and SolarUltraviolet Radiation.

The surface-based observational network of the programme includes Global (31 stations) and Regional (about 400 stations) stations where observations of various GAW parameters occur. These stations are complemented by regular ship cruises and various contributing networks. All observations are linked to common reference standards and the observational data are made available at seven designated World Data Centres (WDC).

Surface-based observations are complemented by airborne and space-based observations that help to characterize the upper troposphere and lower stratosphere, with regards to ozone, solar radiation, aerosols, and certain trace gases.

Requirements to become a GAW station are detailed in the GAW Implementation Plan 2016-2023 (WMO, 2017). A new IP is in preparation, the four strategic objectives will be presented.

• The GAW Quality Management comprises: Data Quality Objectives, Measurement Guidelines, Standard Operating Procedures and Data Quality Indicators. Throughout the programme the common quality assurance principles apply, that include requirements for the long-term sustainability of the observations, use of one network standard for each variable and implementation of the measurement practices that satisfy the set data quality objectives. GAW implements open data policy and requires observational data be made available in the dedicated data centers operated by WMO Member countries.

The programme relies on different types of central facilities: Central Calibration Laboratories, Quality Assurance/Science Activity Centres, World and Regional Calibration Centres, which are also directly supported and implemented by the individual Member countries for the global services.

Majority of the recommendations regarding measurement and quality assurance procedures are developed by the expert and advisory groups within the programme, often those rely on the expertise withing the contributing networks and collaborating organizations, like the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) or the Integrated Carbon Observation System (ICOS).

One of the GAW priorities is to expand and strengthen partnerships with contributing networks, through development of statements and strategies to articulate the mutual benefits for the collaborations and stream-line processes of data reporting and exchange of QA standards and metadata. This involves collaboration with national and regional environmental protection agencies and the development of harmonized metadata and data exchange and quality information.

How to cite: Moreno, S.: The WMO Global Atmosphere Watch Programme new implementation plan and strategic objectives, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14442, https://doi.org/10.5194/egusphere-egu23-14442, 2023.