Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications

Measurement of reactive air pollutants and greenhouse gases underpin a huge variety of applications that span from academic research through to regulatory functions and services for individuals, governments, and businesses. Whilst the vast majority of these observations continue to use established analytical reference methods, miniaturization has led to a growth in the prominence of a generation of devices that are often described generically as “low-cost sensors” (LCSs). LCSs can in practice have other valuable features other than cost that differentiate them from previous technologies including being of smaller size, lower weight and having reduced power consumption. Different technologies falling within this class include passive electrochemical and metal oxide sensors that may have costs of only a few dollars each, through to more complex microelectromechanical devices that use the same analytical principles as reference instruments, but in smaller size and power packages. As a class of device, low-cost sensors encompass a very wide range of technologies and as a consequence they produce a wide range of quality of measurements. When selecting a LCS approach for a particular task, users need to ensure the specific sensor to be used will meet application’s data quality requirements. This report considers sensors that are designed for the measurement of atmospheric composition at ambient concentrations focusing on reactive gaseous air pollutants (CO, NOx, O3, SO2), particulate matter (PM) and greenhouse gases CO2 and CH4. It examines example applications where new scientific and technical insight may potentially be gained from using a network of sensors when compared to more sparsely located observations. Access to low-cost sensors appears to offer exciting new atmospheric applications, can support new services and potentially facilitates the inclusion of a new cohort of users. Based on the scientific literature available up to the end of 2017, it is clear however that some trade-offs arise when LCSs are used in place of existing reference methods. Smaller and/or lower cost devices tend to be less sensitive, less precise and less chemically-specific to the compound or variable of interest. This is balanced by a potential increase in the spatial density of measurements that can be achieved by a network of sensors. The current state of the art in terms of accuracy, reliability and reproducibility of a range of different sensors is described along with the key analytical principles and what has been learned so far about low-cost sensors from both laboratory studies and real-world tests. A summary of concepts is included on how sensors and reference instruments may be used together, as well as with modelling in a complementary way, to improve data quality and generate additional insight into pollution behaviour. The report provides some advice on key considerations when matching a project/study/application with an appropriate sensor monitoring strategy, and the wider application-specific requirements for calibration and data quality. The report contains a number of suggestions on future requirements for low-cost sensors aimed at manufacturers and users and for the broader atmospheric community. The report highlights that low-cost sensors are not currently a direct substitute for reference instruments, especially for mandatory purposes; they are however a complementary source of information on air quality, provided an appropriate sensor is used. It is important for prospective users to identify their specific application needs first, examine examples of studies or deployments that share similar characteristics, identify the likely limitations associated with using LCSs and then evaluate whether their selected LCS approach/technology would sufficiently meet the needs of the measurement objective. Previous studies in both the laboratory and field have shown that data quality from LCSs are highly variable and there is no simple answer to basic questions like “are low-cost sensors reliable?”. Even when the same basic sensor components are used, real-world performance can vary due to different data correction and calibration approaches. This can make the task of understanding data quality very challenging for users, since good or bad performance demonstrated from one device or commercial supplier does not mean that similar devices from others will work the same way. Manufacturers should provide information on their characterizations of sensors and sensor system performance in a manner that is as comprehensive as possible, including results from in-field testing. Reporting of that data should where possible parallel the metrics used for reference instrument specifications, including information on the calibration conditions. Whilst not all users will actively use this information it will support the general development framework for LCS use. Openness in assessment of sensor performance across varying environmental conditions would be very valuable in guiding new user applications and help the field develop more rapidly. Users and operators of low-cost sensors should have a clearly-defined application scope and set of questions they wish to address prior to selection of a sensor approach. This will guide the selection of the most appropriate technology to support a project. Renewed efforts are needed to enhance engagement and sharing of knowledge and skills between the data science community, the atmospheric science community and others to improve LCS data processing and analysis methods. Improved information sharing between manufacturers and user communities should be supported through regular dialogue on emerging issues related to sensor performance, best practice and applications. Adoption of open access and open data policies to further facilitate the development, applications, and use of LCS data is essential. Such practices would facilitate exchange of information among the wide range of interested communities including national/local government, research, policy, industry, and public, and encourage accountability for data quality and any resulting advice derived from LCS data. This assessment was initiated at the request of the WMO Commission for Atmospheric Sciences (CAS) and supported by broader stakeholder atmospheric community including the International Global Atmospheric Chemistry (IGAC) project, Task Force on Measurement and Modelling of the European Monitoring and Evaluation Programme of the LRTAP Convention, UN Environment, World Health Organization, Network of Air Quality Reference Laboratories of the European Commission (AQUILA)

Long-term monitoring of black carbon across Germany

Lately, black carbon (BC) has received significant attention due to its climate-warming properties and adverse health effects. Nevertheless, long-term observations in urban areas are scarce, most likely because BC monitoring is not required by environmental legislation. This, however, handicaps the evaluation of air quality models which can be used to assess the effectiveness of policy measures which aim to reduce BC concentrations. Here, we present a new dataset of atmospheric BC measurements from Germany constructed from over six million measurements at over 170 stations. Data covering the period between 1994 and 2014 were collected from twelve German Federal States and the Federal Environment Agency, quality checked and harmonized into a database with comprehensive metadata. The final data in original time resolution are available for download (https://doi.org/10.1594/PANGAEA.881173). Though assembled in a consistent way, the dataset is characterized by differences in (a) measurement methodologies for determining evolved carbon and optical absorption, (b) covered time periods, and (c) temporal resolutions that ranged from half hourly to measurements every 6th day. Usage and interpretation of this dataset thus requires a careful consideration of these differences. Our analysis focuses on 2009, the year with the largest data coverage with one single methodology, as well as on the relative changes in long-term trends over ten years. For 2009, we find that BC concentrations at traffic sites were at least twice as high as at urban background, industrial and rural sites. Weekly cycles are most prominent at traffic stations, however, the presence of differences in concentrations during the week and on weekends at other station types suggests that traffic plays an important role throughout the full network. Generally higher concentrations and weaker weekly cycles during the winter months point towards the influence of other sources such as domestic heating. Regarding the long-term trends, advanced statistical techniques allow us to account for instrumentation changes and to separate seasonal and long-term changes in our dataset. Analysis shows a downward trend in BC at nearly all locations and in all conditions, with a high level of confidence for the period of 2005–2014. In depth analysis indicates that background BC is decreasing slowly, while the occurrences of high concentrations are decreasing more rapidly. In summary, legislation – both in Europe and locally – to reduce particulate emissions and indirectly BC appear to be working, based on this analysis. Adverse human health and climate impacts are likely to be diminished because of the improvements in air quality

Analysis of the distributions of hourly NO2 concentrations contributing to annual average NO2 concentrations across the European monitoring network between 2000 and 2014

Exposure to nitrogen dioxide (NO2) is associated with negative human health effects, both for short-term peak concentrations and from long-term exposure to a wider range of NO2 concentrations. For the latter, the European Union has established an air quality limit value of 40 µg m−3 as an annual average. However, factors such as proximity and strength of local emissions, atmospheric chemistry, and meteorological conditions mean that there is substantial variation in the hourly NO2 concentrations contributing to an annual average concentration. The aim of this analysis was to quantify the nature of this variation at thousands of monitoring sites across Europe through the calculation of a standard set of chemical climatology statistics. Specifically, at each monitoring site that satisfied data capture criteria for inclusion in this analysis, annual NO2 concentrations, as well as the percentage contribution from each month, hour of the day, and hourly NO2 concentrations divided into 5 µg m−3 bins were calculated. Across Europe, 2010–2014 average annual NO2 concentrations (NO2AA) exceeded the annual NO2 limit value at 8 % of > 2500 monitoring sites. The application of this chemical climatology approach showed that sites with distinct monthly, hour of day, and hourly NO2 concentration bin contributions to NO2AA were not grouped into specific regions of Europe, furthermore, within relatively small geographic regions there were sites with similar NO2AA, but with differences in these contributions. Specifically, at sites with highest NO2AA, there were generally similar contributions from across the year, but there were also differences in the contribution of peak vs. moderate hourly NO2 concentrations to NO2AA, and from different hours across the day. Trends between 2000 and 2014 for 259 sites indicate that, in general, the contribution to NO2AA from winter months has increased, as has the contribution from the rush-hour periods of the day, while the contribution from peak hourly NO2 concentrations has decreased. The variety of monthly, hour of day and hourly NO2 concentration bin contributions to NO2AA, across cities, countries and regions of Europe indicate that within relatively small geographic areas different interactions between emissions, atmospheric chemistry and meteorology produce variation in NO2AA and the conditions that produce it. Therefore, measures implemented to reduce NO2AA in one location may not be as effective in others. The development of strategies to reduce NO2AA for an area should therefore consider (i) the variation in monthly, hour of day, and hourly NO2 concentration bin contributions to NO2AA within that area; and (ii) how specific mitigation actions will affect variability in hourly NO2 concentrations

Tropospheric Ozone Assessment Report : Present-day ozone distribution and trends relevant to human health

This study quantifies the present-day global and regional distributions (2010–2014) and trends (2000–2014) for five ozone metrics relevant for short-term and long-term human exposure. These metrics, calculated by the Tropospheric Ozone Assessment Report, are: 4th highest daily maximum 8-hour ozone (4MDA8); number of days with MDA8 > 70 ppb (NDGT70), SOMO35 (annual Sum of Ozone Means Over 35 ppb) and two seasonally averaged metrics (3MMDA1; AVGMDA8). These metrics were explored at ozone monitoring sites worldwide, which were classified as urban or non-urban based on population and nighttime lights data.Present-day distributions of 4MDA8 and NDGT70, determined predominantly by peak values, are similar with highest levels in western North America, southern Europe and East Asia. For the other three metrics, distributions are similar with North–South gradients more prominent across Europe and Japan. Between 2000 and 2014, significant negative trends in 4MDA8 and NDGT70 occur at most US and some European sites. In contrast, significant positive trends are found at many sites in South Korea and Hong Kong, with mixed trends across Japan. The other three metrics have similar, negative trends for many non-urban North American and some European and Japanese sites, and positive trends across much of East Asia. Globally, metrics at many sites exhibit non-significant trends. At 59% of all sites there is a common direction and significance in the trend across all five metrics, whilst 4MDA8 and NDGT70 have a common trend at ~80% of all sites. Sensitivity analysis shows AVGMDA8 trends differ with averaging period (warm season or annual). Trends are unchanged at many sites when a 1995–2014 period is used; although fewer sites exhibit non-significant trends. Over the longer period 1970–2014, most Japanese sites exhibit positive 4MDA8/SOMO35 trends. Insufficient data exist to characterize ozone trends for the rest of Asia and other world regions

BAERLIN2014 - stationary measurements and source apportionment at an urban background station in Berlin, Germany

The Berlin Air quality and Ecosystem Research: Local and long-range Impact of anthropogenic and Natural hydrocarbons (BAERLIN2014) campaign was conducted during the 3 summer months (June–August) of 2014. During this measurement campaign, both stationary and mobile measurements were undertaken to address complementary aims. This paper provides an overview of the stationary measurements and results that were focused on characterization of gaseous and particulate pollution, including source attribution, in the Berlin–Potsdam area, and quantification of the role of natural sources in determining levels of ozone and related gaseous pollutants. Results show that biogenic contributions to ozone and particulate matter are substantial. One indicator for ozone formation, the OH reactivity, showed a 31% (0.82±0.44s−1) and 75% (3.7±0.90s−1) contribution from biogenic non-methane volatile organic compounds (NMVOCs) for urban background (2.6±0.68s−1) and urban park (4.9±1.0s−1) location, respectively, emphasizing the importance of such locations as sources of biogenic NMVOCs in urban areas. A comparison to NMVOC measurements made in Berlin approximately 20 years earlier generally show lower levels today for anthropogenic NMVOCs. A substantial contribution of secondary organic and inorganic aerosol to PM10 concentrations was quantified. In addition to secondary aerosols, source apportionment analysis of the organic carbon fraction identified the contribution of biogenic (plant-based) particulate matter, as well as primary contributions from vehicles, with a larger contribution from diesel compared to gasoline vehicles, as well as a relatively small contribution from wood burning, linked to measured levoglucosan

Analysis of long-term observations of NOx and CO in megacities and application to constraining emissions inventories

Long-term atmospheric NOx/CO enhancement ratios in megacities provide evaluations of emission inventories. A fuel-based emission inventory approach that diverges from conventional bottom-up inventory methods explains 1970–2015 trends in NOx/CO enhancement ratios in Los Angeles. Combining this comparison with similar measurements in other U.S. cities demonstrates that motor vehicle emissions controls were largely responsible for U.S. urban NOx/CO trends in the past half century. Differing NOx/CO enhancement ratio trends in U.S. and European cities over the past 25 years highlights alternative strategies for mitigating transportation emissions, reflecting Europe's increased use of light-duty diesel vehicles and correspondingly slower decreases in NOx emissions compared to the U.S. A global inventory widely used by global chemistry models fails to capture these long-term trends and regional differences in U.S. and Europe megacity NOx/CO enhancement ratios, possibly contributing to these models' inability to accurately reproduce observed long-term changes in tropospheric ozone

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.Peer reviewedFinal Published versio

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.World Meteorological Organization Global Atmospheric Watch programme is gratefully acknowledged for initiating and coordinating this study and for supporting this publication. We acknowledge the following projects for supporting the analysis contained in this article: Air Pollution and Human Health for an Indian Megacity project PROMOTE funded by UK NERC and the Indian MOES, Grant reference number NE/P016391/1; Regarding project funding from the European Commission, the sole responsibility of this publication lies with the authors. The European Commission is not responsible for any use that may be made of the information contained therein. This project has received funding from the European Commission’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Estonian Research Council (project PRG714); Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (KKOBS, project 2014-2020.4.01.20-0281). European network for observing our changing planet project (ERAPLANET, grant agreement no. 689443) under the European Union’s Horizon 2020 research and innovation program, Estonian Ministry of Sciences projects (grant nos. P180021, P180274), and the Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (3.2.0304.11-0395). Eastern Mediterranean and Middle East—Climate and Atmosphere Research (EMME-CARE) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612) and the Government of Cyprus. INAR acknowledges support by the Russian government (grant number 14.W03.31.0002), the Ministry of Science and Higher Education of the Russian Federation (agreement 14.W0331.0006), and the Russian Ministry of Education and Science (14.W03.31.0008). We are grateful to to the following agencies for providing access to data used in our analysis: A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences; Agenzia Regionale per la Protezione dell’Ambiente della Campania (ARPAC); Air Quality and Climate Change, Parks and Environment (MetroVancouver, Government of British Columbia); Air Quality Monitoring & Reporting, Nova Scotia Environment (Government of Nova Scotia); Air Quality Monitoring Network (SIMAT) and Emission Inventory, Mexico City Environment Secretariat (SEDEMA); Airparif (owner & provider of the Paris air pollution data); ARPA Lazio, Italy; ARPA Lombardia, Italy; Association Agr´e´ee de Surveillance de la Qualit´e de l’Air en ˆIle-de- France AIRPARIF / Atmo-France; Bavarian Environment Agency, Germany; Berlin Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, Germany; California Air Resources Board; Central Pollution Control Board (CPCB), India; CETESB: Companhia Ambiental do Estado de S˜ao Paulo, Brazil. China National Environmental Monitoring Centre; Chandigarh Pollution Control Committee (CPCC), India. DCMR Rijnmond Environmental Service, the Netherlands. Department of Labour Inspection, Cyprus; Department of Natural Resources Management and Environmental Protection of Moscow. Environment and Climate Change Canada; Environmental Monitoring and Science Division Alberta Environment and Parks (Government of Alberta); Environmental Protection Authority Victoria (Melbourne, Victoria, Australia); Estonian Environmental Research Centre (EERC); Estonian University of Life Sciences, SMEAR Estonia; European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Finnish Meteorological Institute; Helsinki Region Environmental Services Authority; Haryana Pollution Control Board (HSPCB), IndiaLondon Air Quality Network (LAQN) and the Automatic Urban and Rural Network (AURN) supported by the Department of Environment, Food and Rural Affairs, UK Government; Madrid Municipality; Met Office Integrated Data Archive System (MIDAS); Meteorological Service of Canada; Minist`ere de l’Environnement et de la Lutte contre les changements climatiques (Gouvernement du Qu´ebec); Ministry of Environment and Energy, Greece; Ministry of the Environment (Chile) and National Weather Service (DMC); Moscow State Budgetary Environmental Institution MOSECOMONITORING. Municipal Department of the Environment SMAC, Brazil; Municipality of Madrid public open data service; National institute of environmental research, Korea; National Meteorology and Hydrology Service (SENAMHI), Peru; New York State Department of Environmental Conservation; NSW Department of Planning, Industry and Environment; Ontario Ministry of the Environment, Conservation and Parks, Canada; Public Health Service of Amsterdam (GGD), the Netherlands. Punjab Pollution Control Board (PPCB), India. R´eseau de surveillance de la qualit´e de l’air (RSQA) (Montr´eal); Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics, Russia; Russian Foundation for Basic Research (project 20–05–00254) SAFAR-IITM-MoES, India; S˜ao Paulo State Environmental Protection Agency, CETESB; Secretaria de Ambiente, DMQ, Ecuador; Secretaría Distrital de Ambiente, Bogot´a, Colombia. Secretaria Municipal de Meio Ambiente Rio de Janeiro; Mexico City Atmospheric Monitoring System (SIMAT); Mexico City Secretariat of Environment, Secretaría del Medio Ambiente (SEDEMA); SLB-analys, Sweden; SMEAR Estonia station and Estonian University of Life Sciences (EULS); SMEAR stations data and Finnish Center of Excellence; South African Weather Service and Department of Environment, Forestry and Fisheries through SAAQIS; Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO); University of Helsinki, Finland; University of Tartu, Tahkuse air monitoring station; Weather Station of the Institute of Astronomy, Geophysics and Atmospheric Science of the University of S˜ao Paulo; West Bengal Pollution Control Board (WBPCB).http://www.elsevier.com/locate/envintam2023Geography, Geoinformatics and Meteorolog

Exposure to air pollution in Germany for the decade 2010-2019 (APExpose_DE)

In this dataset we present metrics related to the exposure to air pollution in Germany for the decade 2010-2019. The sources used for the production of the dataset were Airbase, from the European Environmental Agency (https://www.eea.europa.eu/themes/air/explore-air-pollution-data) and the CAMS (Copernicus Atmosphere Monitoring Service) global reanalysis EAC4 (https://www.ecmwf.int/en/forecasts/dataset/cams-global-reanalysis). Stations of the types "Traffic" and "Industrial" were left out for being considered unrepresentative to long-term exposure, those of the type "Background" were included. Each station was geo-located within, and each computed yearly value associated to, a NUTS-3 unit. Within each NUTS-3 (Nomenclature of Territorial Units for Statistics) unit and for each metric, the yearly values per station were averaged in three ways, giving preference to different station sitings, each representing a different scenario: average, urban, remote. The monitoring data were produced for the NUTS-3 units and the years where monitoring data for a given pollutant is available. In order to complete the dataset for the NUTS-3 units where no monitoring data for a given pollutant is available, the Copernicus Atmospheric Monitoring Service (CAMS) EAC4 reanalysis (https://www.ecmwf.int/en/forecasts/dataset/cams-global-reanalysis) was used. The yearly-averaged rasters from CAMS were vectorized and scaled to available monitoring data to obtain values for each NUTS-3 units. As a final step, the Airbase and CAMS derived data were combined to produce the APExpose_DE dataset. Each record (each line in the file) corresponds to a NUTS-3 unit (identified by its name and its code), and a scenario, for a given year. There are 402 NUTS-3 units in Germany and 3 scenarios were developed, the total number of records in the dataset is 1206 per year, or 12060 for the entire study period. Each record includes a numeric value for each metric considered. The ASCII format of the provided dataset enables a simple access and workup. The NUTS-3 code, provided for each record, enables linking the dataset to other, possibly vectorized, datasets at the NUTS-3 or coarser level

A survey on the perceived need and value of decision-support tools for joint mitigation of air pollution and climate change in cities

Decision-support tools are increasingly popular for informing policy decisions linked to environmental issues. For example, a number of decision-support tools on transport planning provide information on expected effects of different measures (actions, policies, or interventions) on air quality, often combined with information on noise pollution or mitigation costs. These tools range in complexity and scale of applicability, from city to international, and include one or several polluting sectors. However, evaluation of the need and utility of tools to support decisions on such linked issues is often lacking, especially for tools intended to support local authorities at the city scale. Here we assessed the need for and value of combining air pollution and climate change mitigation measures into one decision-support tool and the existing policy context in which such a tool might be used. We developed a prototype decision-support tool for evaluating measures for coordinated management of air quality and climate change; and administered a survey in which respondents used the prototype to answer questions about demand for such tools and requirements to make them useful. Additionally, the survey asked questions about participants’ awareness of linkages between air pollution and climate change that are crucial for considering synergies and trade-offs among mitigation measures. Participants showed a high understanding of the linkages between air pollution and climate change, especially recognizing that emissions of greenhouse gases and air pollutants come from the same source. Survey participants were: European, predominantly German; employed across a range of governmental, non-governmental and research organizations; and responsible for a diversity of issues, primarily involving climate change, air pollution or environment. Survey results showed a lack of awareness of decision-support tools and little implementation or regular use. However, respondents expressed a general need for such tools while also recognizing barriers to their implementation, such as limited legal support or lack of time, finances, or manpower. The main barrier identified through this study is the mismatch between detailed information needed from such tools to make them useful at the local implementation scale and the coarser scale information readily available for developing such tools. Significant research efforts at the local scale would be needed to populate decision-support tools with salient mitigation alternatives at the location of implementation. Although global- or regional-scale information can motivate local action towards sustainability, effective on-the-ground implementation of coordinated measures requires knowledge of local circumstances and impacts, calling for active engagement of the local research communities