For reducing health impacts from air pollution, it is important to know the sources contributing to human exposure. This study systematically reviewed and analysed available source apportionment studies on particulate matter (of diameter of 10 and 2.5 microns, PM10 and PM2.5) performed in cities to estimate typical shares of the sources of pollution by country and by region. A database with city source apportionment records, estimated with the use of receptor models, was also developed and available at the website of the World Health Organization. Systematic Scopus and Google searches were performed to retrieve city studies of source apportionment for particulate matter. Six source categories were defined. Country and regional averages of source apportionment were estimated based on city population weighting. A total of 419 source apportionment records from studies conducted in cities of 51 countries were used to calculate regional averages of sources of ambient particulate matter. Based on the available information, globally 25% of urban ambient air pollution from PM2.5 is contributed by traffic, 15% by industrial activities, 20% by domestic fuel burning, 22% from unspecified sources of human origin, and 18% from natural dust and salt. The available source apportionment records exhibit, however, important heterogeneities in assessed source categories and incompleteness in certain countries/regions. Traffic is one important contributor to ambient PM in cities. To reduce air pollution in cities and the substantial disease burden it causes, solutions to sustainably reduce ambient PM from traffic, industrial activities and biomass burning should urgently be sought. However, further efforts are required to improve data availability and evaluation, and possibly to combine with other types of information in view of increasing usefulness for policy making.JRC.H.2-Air and Climat

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R² > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

This study provides improved methanol emission estimates on the global scale, in particular for the largest methanol source, the terrestrial biosphere, and for biomass burning. To this purpose, one complete year of spaceborne measurements of tropospheric methanol columns retrieved for the first time by the thermal infrared sensor IASI aboard the MetOp satellite are compared with distributions calculated by the IMAGESv2 global chemistry-transport model. Two model simulations are performed using a priori biogenic methanol emissions either from the new MEGANv2.1 emission model, which is fully described in this work and is based on net ecosystem flux measurements, or from a previous parameterization based on net primary production by Jacob et al. (2005). A significantly better model performance in terms of both amplitude and seasonality is achieved through the use of MEGANv2.1 in most world regions, with respect to IASI data, and to surface- and air-based methanol measurements, even though important discrepancies over several regions are still present. As a second step of this study, we combine the MEGANv2.1 and the IASI column abundances over continents in an inverse modelling scheme based on the adjoint of the IMAGESv2 model to generate an improved global methanol emission source. The global optimized source totals 187 Tg yr⁻¹ with a contribution of 100 Tg yr⁻¹ from plants, only slightly lower than the a priori MEGANv2.1 value of 105 Tg yr⁻¹. Large decreases with respect to the MEGANv2.1 biogenic source are inferred over Amazonia (up to 55 %) and Indonesia (up to 58 %), whereas more moderate reductions are recorded in the Eastern US (20–25 %) and Central Africa (25–35 %). On the other hand, the biogenic source is found to strongly increase in the arid and semi-arid regions of Central Asia (up to a factor of 5) and Western US (factor of 2), probably due to a source of methanol specific to these ecosystems which is unaccounted for in the MEGANv2.1 inventory. The most significant error reductions achieved by the optimization concern the derived biogenic emissions over the Amazon and over the Former Soviet Union. The robustness of the derived fluxes to changes in convective updraft fluxes, in methanol removal processes, and in the choice of the biogenic a priori inventory is assessed through sensitivity inversions. Detailed comparisons of the model with a number of aircraft and surface observations of methanol, as well as new methanol measurements in Europe and in the Reunion Island show that the satellite-derived methanol emissions improve significantly the agreement with the independent data, giving thus credence to the IASI dataset.