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, O-3 and the total gaseous oxidant (O-X = NO2 + O-3) 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 O-3 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 similar to 70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between similar to 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 similar 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 similar to 60%). Analysis of the total oxidant (OX = NO2 + O-3) showed that primary NO2 emissions at urban locations were greater than the O-3 production, whereas at background sites, O-X was mostly driven by the regional contributions rather than local NO2 and O-3 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.

Air pollution is among the top threats to human health and ecosystems despite the substantial decrease in anthropogenic emissions. Meanwhile, the role of ship emissions on air quality is becoming increasingly important with the growing maritime transport and less strict regulations. In this study, we modeled the air quality in Europe between 1990 and 2030 with ten-year intervals, using the regional air quality model CAMx version 6.50, to investigate the changes in the past (1990-2010) as well as the effects of different land and ship emission scenarios in the future (2020,2030). The modeled mean ozone levels decreased slightly during the first decade but then started increasing again especially in polluted areas. Results from the future scenarios suggest that by 2030 the peak ozone would decrease, leading to a decrease in the days exceeding the maximum daily 8-h average ozone (MDA8) limit values (60 ppb) by 51% in southern Europe relative to 1990. The model results show a decrease of 56% (6.3 mu g m(-3)) in PM2.5 concentrations from 1990 to 2030 under current legislation, mostly due to a large drop in sulfate (representing up to 44% of the total PM2.5 decrease during 1990-2000) while nitrate concentrations were predicted to go down with an increasing rate (10% of total PM2.5 decrease during 1990-2000 while 36% during 2020-2030). The ship emissions if reduced according to the maximum technically feasible reduction (MTFR) scenario were predicted to contribute up to 19% of the decrease in the PM2.5 concentrations over land between 2010 and 2030. Ship emission reductions according to the MTFR scenario would lead to a decrease in the days with MDA8 exceeding EU limits by 24-28% (10-14 days) around the coastal regions. The results obtained in our study show the increasing importance of ship emission reductions, after a relatively large decrease in land emissions was achieved in Europe. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Health effect estimates depend on the methods of evaluating exposures. Due to non-linearities in the exposure-response relationships, both the predicted mean exposures as well as its spatial variability are significant. The aim of this work is to systematically quantify the impact of the spatial resolution on population-weighted mean concentration (PWC), its variance, and mortality attributable to fine particulate matter (PM2.5) exposure in Finland in 2015. The atmospheric chemical transport model SILAM was used to estimate the ambient air PM2.5 concentrations at 0.02° longitudinal × 0.01° latitudinal resolution (ca. 1 km), including both the national PM2.5 emissions and the long-range transport. The decision-support model FRES source-receptor matrices applied at 250-m resolution was used to model the ambient air concentrations of primary fine particulate matter (PPM2.5) from local and regional sources up to 10 km and 20 km distances. Numerical averaging of population and concentrations was used to produce the results for coarser resolutions. Population-weighted PM2.5 concentration was 11% lower at a resolution of 50 km, compared with the corresponding computations at a resolution of 1 km. However, considering only the national emissions, the influences of spatial averaging were substantially larger. The average population-weighted local PPM2.5 concentration originated from Finnish sources was 70% lower at a resolution of 50 km, compared with the corresponding result obtained using a resolution of 250 m. The sensitivity to spatial averaging, between the finest 250-m and the coarsest 50-km resolution, was highest for the emissions of PPM2.5 originated from national vehicular traffic (about 80% decrease) and lowest for the national residential combustion (60% decrease). Exposure estimates in urban areas were more sensitive to the changes of model resolution (14% and 74% decrease for PM2.5 and local PPM2.5, respectively), compared with estimates in rural areas (2% decrease for PM2.5 and 36% decrease for PPM2.5). We conclude that for the evaluation of the health impacts of air pollution, the resolution of the model computations is an important factor, which can potentially influence the predicted health impacts by tens of percent or more, especially when assessing the impacts of national emissions.

Katupölyn syntyyn vaikuttavat monet tekijät, ja sen on tutkimusten mukaan osoitettu olevan pääosin mineraaliperäistä karkean kokoluokan pölyä. Ongelmaksi katupöly muodostuu silloin, kun hiukkaset päätyvät hengitysilmaan ja aiheuttavat sitä kautta viihtyvyys- ja terveyshaittoja, erityisesti kaupunkien asukkaille. Tutkimushankkeen ”Katupölyn lähteet, päästövähennyskeinot ja ilmanlaatuvaikutukset” (KALPA) tavoitteena oli selvittää katupölypitoisuuksiin vaikuttavia tekijöitä ja eri lähteiden osuuksia erilaisissa katukohteissa sekä tunnistaa lupaavampia päästövähennysmahdollisuuksia ja edesauttaa niiden käyttöönottoa. Tämä raportti käsittelee KALPA-hankkeen kolmannessa vaiheessa (KALPA3), vuosina 2019–2020, tehtyjä tutkimuksia. KALPA-hankkeen neljän ensimmäisen vuoden tulokset on aiemmin (2019) raportoitu HSY:n julkaisusarjassa. KALPA-hanke on jatkoa aiemmille REDUST- ja KAPU- katupölyhankkeille. Hankkeen toteuttajat olivat Suomen ympäristökeskus (SYKE) ja Metropolia Ammattikorkeakoulu. Hankkeessa käytettiin erilaisia metodeja katupölypäästöjen tarkasteluun. Metropolia Ammattikorkeakoulun Nuuskija-autolla mitattiin katupölypäästöjä (PM10 ja PM2,5, eli halkaisijaltaan alle 10 tai alle 2,5 mikrometrin hiukkaset) kaupunkien katuverkoilla, työmaakohteissa ja korkean nopeuden pääväyläkohteissa sekä testattiin erilaisten pesulaitteiden ja -menetelmien tehoa pölynpoistossa. Mittauksia täydennettiin toisella mittausajoneuvolla (TRAKER-menetelmään perustuva Vectra) ja Wet Dust Samplerilla (WDS) suoritetuilla mittauksilla. WDS-mittalaitteella saadaan tietoa tienpinnassa olevasta pölyvarastosta, ja sitä hyödynnettiin mm. pesulaitetestien yhteydessä. KALPA-hankkeessa aikaisemmin tehtyjen nastarengasmittausten tuloksia verrattiin yliajotestillä saatuihin renkaiden kulumatuloksiin. Lisäksi hankkeessa käytettiin NORTRIP-katupölymallia hiekoituksen ilmanlaatuvaikutuksen tarkasteluun ja katupölyn lähteiden arvioimiseen sekä havainnollistettiin nastarengasosuuden laskemisen vaikutusta ilmanlaatuun hengitettävien hiukkasten osalta. NORTRIP-mallilla laskettuja päästökertoimia käytettiin lisäksi kansallisessa FRES-mallissa, jonka avulla arvioitiin katupölyn merkittävyyttä koko Suomen tasolla. FRES-mallilla toteutetun arvion mukaan katupölypäästöt muodostivat 34 % PM10-kokonaispäästöistä ja 5,5 % PM2,5- kokonaispäästöistä Suomessa vuonna 2015.

Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen's slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM2.5 and PNC showed significant negative monotonic trends (0.03-0.6 mg m(-3).yr(-1) and 0.40-3.8 x 10(3) particles. cm(-3). yr(-1), respectively) except Brisbane (+0.1 mg m(-3). yr(-1) and +53 particles. cm(-3). yr(-1), respectively). For the period covered in this study, temperature increased (0.03-0.07 degrees C.yr(-1)) in all cities except London; precipitation decreased (0.02-1.4 mm.yr(-1)) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management. (C) 2020 Elsevier Ltd. All rights reserved.

China is one of the regions with highest PM(2.5)concentration in the world. In this study, we review the spatio-temporal distribution of PM2.5 mass concentration and components in China and the effect of control measures on PM2.5 concentrations. Annual averaged PM2.5 concentrations in Central-Eastern China reached over 100 mu g m(-3), in some regions even over 150 mu g m(-3). In 2013, only 4.1% of the cities attained the annual average standard of 35 mu g m(-3). Aitken mode particles tend to dominate the total particle number concentration. Depending on the location and time of the year, new particle formation (NPF) has been observed to take place between about 10 and 60% of the days. In most locations, NPF was less frequent at high PM mass loadings. The secondary inorganic particles (i.e., sulfate, nitrate and ammonium) ranked the highest fraction among the PM2.5 species, followed by organic matters (OM), crustal species and element carbon (EC), which accounted for 6-50%, 15-51%, 5-41% and 2-12% of PM2.5, respectively. In response to serious particulate matter pollution, China has taken aggressive steps to improve air quality in the last decade. As a result, the national emissions of primary PM2.5, sulfur dioxide (SO2), and nitrogen oxides (NOx) have been decreasing since 2005, 2006, and 2011, respectively. The emission control policies implemented in the last decade could result in noticeable reduction in PM2,(5)concentrations, contributing to the decreasing PM2.5 trends observed in Beijing, Shanghai, and Guangzhou. However, the control policies issued before 2010 are insufficient to improve PM2.5 air quality notably in future. An optimal mix of energy-saving and end-of-pipe control measures should be implemented, more ambitious control policies for NMVOC and NH3 should be enforced, and special control measures in winter should be applied. 40-70% emissions should be cut off to attain PM2.5 standard. (C) 2017 Elsevier B.V.All rights reserved.

Despite much effort made in studying human health associated with fine particulate matter (PM2.5), our knowledge about PM2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM2.5 at individual stations (SC-PM2.5). Then we merged SC-PM2.5 with the spatial pattern of GEOS-Chem modeled PM2.5 to obtain a gridded PM2.5 dataset (GC-PM2.5). We validated the GC-PM2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM2.5 measurements. The spatial patterns of temporally averaged PM2.5 mass concentrations are consistent between GC-PM2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM2.5 data reproduce the day-to-day variation of observed PM2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM2.5 variations and their health impacts since 1980.

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R2=0.77) and TDNN for hourly averaged data (with R2=0.66) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

It is often stated that plants remove air pollutants from the urban atmosphere with their large leaf area, thus providing benefits − i.e. ecosystem services − for citizens. However, empirical evidence showing that local-scale air quality is uniformly improved by urban forests is scarce. We studied the influence of conifer-dominated peri-urban forests on the springtime levels of NO2 and particle pollution at different distances from roads, using passive samplers and high time resolution particle counters in a northern climate in Finland. Passive samplers provided average values over a one month period, while active particle counters provided real time measurements of air pollution to mimic human inhalation frequency. NO2 concentrations were slightly higher in forests than in adjacent open areas, while passive particle measurements showed the opposite trend. Active particle monitoring campaigns showed no systematic forest effect for PM2.5, but larger particles were reduced in the forest, corroborating the passive sampling result. Attenuation rates of the mean values of the studied pollutants did not differ between the forest and open habitats. However, high time resolution particle data revealed a distance effect that was apparent only in the forest transect: peak events at the forest edge were higher, while peaks furthest from the road were lower compared to the open transect. Furthermore, the magnitude of PM2.5 peak events was distinctly higher at forest edge than equivalent distance in the open area. Vegetation characteristics, such as canopy cover and tree density, did not explain differences in pollutant levels in majority of cases. Our results imply that evergreen-dominated forests near roads can slightly worsen local air quality regarding NO2 and PM2.5 in northern climates, but that coarser particle pollution can be reduced by such forest vegetation. It seems that the potential of roadside vegetation to mitigate air pollution is largely determined by the vegetation effects on airflow.

Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM2.5 (mass concentration of particles with aerodynamic diameter <2.5 mu m) and factors that influence their concentrations. Hourly average PNC and PM2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 x 10 3 to 19.5 x 10(3) particles.cm(-3) and from 7.0 to 65.8 mu g.m(-3) for PNC and PM2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM2.5 demonstrated that a high PNC level does not lead to a high PM2.5, and vice versa. The particle number-to-mass ratio (in units of 10(9) particles.mu g(-1)) ranged from 0.14 to 2.2, > 1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM2.5. Therefore, PNC and PM2.5 measurements are not representative of each other; and regulating PM2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.