Covid-19 was first reported in Morocco on March 2, 2020. Since then, to prevent its propagation, the Moroccan government declared a state of health emergency. A set of rapid and strict ...countermeasures have taken, including locking down cities, limiting population's mobility and prohibiting almost all avoidable activities. In the present study, we attempted to evaluate the changes in levels of some air pollutants (mainly PM10, NO2 and SO2) in Salé city (North-Western Morocco) during the lockdown measures. In this context, a continuous measurement of PM10, SO2 and NO2 was carried before and during the Covid-19 lockdown period. As a consequence of the security measures and control actions undertaken, the emissions from vehicle exhaust and industrial production were significantly reduced, which contribute to the decrease in the concentrations of the studied pollutants. The obtained results showed that the difference between the concentrations recorded before and during the lockdown period were respectively 75%, 49% and 96% for PM10, SO2 and NO2. PM10 levels were much less reduced than NO2. The three-dimensional air mass backward trajectories, using the HYSPLIT model, demonstrated the benefits of PM10 local emission reductions related to the lockdown were overwhelmed by the contribution of long-range transported aerosols outside areas. In addition, noteworthy differences in the air mass back trajectories and the meteorology between these two periods were evidenced.
Daily average concentrations of SO2 and NO2 from March 11th to April 2nd in Salé city. Display omitted
•PM10, NO2 and SO2 concentrations were reduced by more than half during the covid-19 lockdown period.•Covid-19 countermeasures contribute to reduce all pollutant concentrations but with significant differences among them.•Long-range transported aerosols contributions overcame the PM10 local emission reductions benefits related to the lockdown.
•A high-resolution (1 km) and high-quality PM10 dataset in China (i.e., ChinaHighPM10) is generated.•The ChinaHighPM10 dataset yields a high accuracy (R2 = 0.86, RMSE = 24.28 μg/m3) and outperforms ...most previous studies.•PM10 concentrations significantly decreased by 5.81 μg/m3/yr (p < 0.001) from 2015 to 2019 across China.
Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 μg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.
As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and ...temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM2.5, PM10, and PMcoarse (PM10 - PM2.5) and PM2.5 to PM10 ratios (PM2.5/PM10) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM2.5 and PM10 decreased from 48 to 42 μg m−³ and from 88 to 84 μg m−³, respectively, and the annual mean concentrations were 45 μg m−³ (PM2.5) and 84 μg m−³ (PM10) during the time period (2015–2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PMcoarse concentrations were highest due to dust. The PM2.5/PM10 ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.
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•Areal-temporal patterns of different-sized PM in 366 Chinese cities were analyzed.•Areal-temporal variations of PM2.5/PM10 were explored in China at multiple scales.•Annual mean concentrations of different-sized PM decreased from 2015 to 2017.•Correlations between different-sized PM and meteorology in China were investigated.
Pollution of PM2.5, PM10, and PMcoarse and PM2.5/PM10 ratios and their correlations with meteorological conditions showed great areal-temporal disparities in China.
The main aim of this research effort is to assess the impact of the different circulating vehicle fleets on PM10 pollution, comparing the results from the ten most populated metropolitan cities in ...Italy. Circulating diesel vehicles have been categorized in different groups depending on the vehicle type (car or Light Commercial Vehicle - LCV) and European emission standard. The annual mileage and the total PM10 emission for each category has been determined based on several data sources. Estimated overall annual emissions of PM10 particles have been compared with PM10 concentration measurements from distributed ground monitoring stations. A new index, named SoP (Strength of Pollution), has been defined in order to quantify the contribution of each fleet category to the overall PM10 pollution. The index has been computed for the ten most populated Italian metropolitan cities, i.e. all cities with more than 300.000 inhabits: Rome, Milan, Naples, Turin, Palermo, Genoa, Bologna, Florence, Bari and Catania. Results in terms of SoP estimates for year 2018 reveal the presence in these Italian cities of emission clusters with heterogeneous characteristics, which impose the adoption of different PM10 pollution mitigation approaches in the different cities. For example, in Naples, Catania and Palermo, Euro 0 car fleets emit a total PM10 mass which is respectively 19, 10 and 5 times the mass emitted by Euro 6 vehicles, and consequently a reduction of this fleet is desirable for pollution mitigation purposes. Conversely, in Rome, Genoa and Bari, Euro 3 and 4 car fleets emit a total PM10 mass which is 3–6 times the one emitted by Euro 6 vehicles, which calls for a reduction of these fleets. Thus, the extension to the entire national territory of the results obtained in a specific metropolitan city may be strongly misleading and produce limited effects in terms of pollution mitigation.
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•This study quantifies PM10 pollution emitted by vehicles in urban areas.•The approach was applied to all (10) Italian cities with more than 300,000 inhab.•The approach can have also a potential international applicability.•Vehicle emissions are compared with atmospheric PM10 concentration measurements.•Adoption of different PM10 pollution mitigation approaches in the different cities.
The government decision to promote the change of energy mix has led to a serious decrease in industrial activities in the energy basin of Western Macedonia and subsequently had a positive influence ...on the air quality of the region. To examine the fact and estimate the current atmospheric emission sources, PM10 samples were collected at different sites in Western Macedonia, Greece, from February 2022 to May 2023 and were analyzed for metallic components, major ions, and PAHs. The dataset was then analyzed using PMF and CMB model to identify the possible emission sources. In this study, the first results are presented and discussed.
A recent study by Pini et al. (2021), focusing on year 2018, demonstrated that different strategies should be considered in different Italian cities to mitigate the effects of PM10 pollution produced ...by circulating cars and commercial vehicles. The current study focuses on year 2020, considering the same ten Italian cities. This new study relies on the estimation of specific indices used to assess the size of the different circulating vehicle fleets (vehicle yearly mileage, diesel-fuel car and LCV fleet dimension, etc.) and their impact on PM10 pollution (Strength of Pollution). Results for 2020, severely affected by vehicular restrictions associated with COVID-19, indicate the need to adopt PM10 pollution reduction strategies for the various cities partially different from those identified earlier. For example, Euro 4 cars is the fleet having the highest impact on PM10 pollution in Rome (emitting 3,3 times more than Euro 6 vehicles), while in Milan the most polluting vehicles are Euro 0 cars (emitting 2 times more than Euro 6 vehicles). In Naples, Euro 0 cars emit 12,5 times more than Euro 6 vehicles. A careful look into the results also reveals that, for all considered cities, the three top fleets in terms of PM10 pollution always include Euro 4 or a higher Euro category fleet and a lower Euro category fleet (Euro 0 or Euro 3). These values were validated based on the use of pollution data from ground monitoring stations, which also allowed estimating the atmospheric mixing layer height. Results from the paper suggest that different incentivization policies have to be considered for the different considered cities. For example, in Naples the allocation of incentives should be ~60% towards new vehicles and ~40% towards recent used (i.e. second-hand) non-diesel vehicles, while in Florence it should be ~90% towards ECVs and ~10% towards recent used non-diesel vehicles.
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•This study quantifies PM10 pollution emitted by different vehicle fleets in urban areas.•Approach applied to 10 top populated Italian cities with potential international applicability•Different PM10 mitigation approaches for different fleet categories in the different cities•Strategy for incentive allocation towards new or recent used non-diesel vehicles•Vehicle emissions compared with atmospheric PM10 measurements and mixing layer height
Extreme haze episodes have frequently occurred in Seoul since mid-2010s by the combined contributions of transboundary transported aerosols as well as locally emitted pollutants. In this study, we ...developed a novel method to estimate the contribution of long-range transport (LRT, aerosols are transported from any regions except local area near Seoul) and local pollution (LP, aerosols are originated from local area near Seoul) cases to the PM10 concentration in Seoul, Korea, using the PM10 concentration ratio between surface (PM10S) and mountaintop (PM10M) sites and the lidar-derived mixing layer height. The overall contributions of LRT and LP events to nighttime high-PM10 episodes (PM10 > 50 μg m−3) during the period of May 2008–April 2019 were estimated to be approximately 32% and 47%, respectively. The monthly contribution of LRT events to the PM10 concentration varied from approximately 18% (July) to 43% (January), whereas the contribution of LP events was estimated between 39% (March) and 69% (July); this pattern was associated with seasonal synoptic circulations. The similar PM10S values between the LRT (71 ± 22 μg m−3) and LP (73 ± 26 μg m−3) cases during the nighttime suggest that a reduction in local PM10 emissions is crucial to decrease the PM10 concentration during high-PM10 events. The high PM10S for daytime LRT cases can be explained by the combined effects of increased local emissions and LRT aerosols.
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•Develop a novel method to distinguish between long-range transport (LRT) and local pollution (LP) episodes•Overall contributions of the LRT and LP cases to the high-PM10 episodes were estimated as 32% and 47%, respectively.•Monthly contribution of LRT events to the PM10 concentration varied from approximately 18% (July) to 43% (January).•Contribution of LP events ranged from approximately 39% (March) to 69% (July).•A reduction in the local PM10 emissions is crucial to decrease the PM10 concentration during high-PM10 events.
The composition and amounts of dust or particulate matter (PM) vary greatly in the atmosphere and particulates can have major adverse impacts on human health. Since PM10 deposition to foliage can ...improve air quality, there have been a number of studies of PM10 amounts on the surface of urban vegetation. Much less is known about PM in agricultural areas, although PM10 can be harmful to vegetation and, for food crops, have the potential to be ingested. Here we have quantified PM across the agricultural landscape of California, measuring the amounts present on the foliage of 18 crops at 21 locations. The amounts of particulates present varied (0.17–6.9 gm-2) and PM loads on leaves in the south and east of the area were higher than those in the northern and westerly locations. Our findings suggest that the amounts of particulates on the surface of agricultural crops can be high; exceeding the usual range of values for urban areas. Our data indicate that agricultural crops grown in regions like California and the Mediterranean where summer rainfall is largely absent and drip rather than spray irrigation is used are more vulnerable to PM accumulation and to any adverse effects resulting from these deposits. Microscopic analysis of PM10 on foliage showed that in these agricultural areas wind-blown soil particles make up 74.2% of the PM10 present; this is thus a very significant source. A small number of microplastic particles (2.2%) were also identified on foliage. We suggest that PM and microplastics could impact food and forage quality and that more work is needed to examine PM deposition to crops and their range of impacts.
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•Little is known on particulate matter deposition and impacts in agriculture.•Particulates (PM10) can be harmful to vegetation and to human health if ingested.•Particulates amount on crops can be high; exceeding the usual range in urban areas.•In agriculture soil & microplastics make up 74.2 and 2.2 % of leaf surface dust.•Dust effects food & forage quality; more work is needed on deposition & impacts.
The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM2.5 and PM10) in ...Delhi, India from January 2013 to December 2016. The annual average concentrations of PM2.5 and PM10 were 131 ± 79 μg m−3 (range: 17–417 μg m−3) and 238 ± 106 μg m−3 (range: 34–537 μg m−3), respectively. PM2.5 and PM10 samples were chemically characterized to assess their chemical components i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM2.5 concentration and 24% to the total PM10 concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM2.5 and PM10 were carried by north westerly winds (north-western Indo Gangetic Plains of India).
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•Simultaneous sampling of PM2.5 and PM10 was carried out for 4 years (2013–2016).•Seasonal variations in composition and sources of PM2.5 and PM10 are studied in Delhi.•Secondary inorganic aerosol accounts for 21% of PM10 and 27% of PM2.5 mass with contrasting seasonal variations.•Traffic emission contributes greatly to PM10 while biomass burning to PM2.5, both being maximum in winters.•Maximum concentrations of PM2.5 and PM10 were coming from North West direction of Delhi (CBPF plots).
The present work explores the temporal and seasonal variabilities in composition and contributions of different sources to fine and coarse fractions of particulate matter over Delhi.
•Concentrations of PM and gaseous pollutants in 31 Chinese cities (from 286 sites) were analyzed.•Concentration levels of PMs were significantly different in various cities.•The correlations between ...PMs and NO2, SO2 were moderate.•The correlation between PMs and CO was instable and that between PMs and O3 was weak.
The variations of mass concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 in 31 Chinese provincial capital cities were analyzed based on data from 286 monitoring sites obtained between March 22, 2013 and March 31, 2014. By comparing the pollutant concentrations over this length of time, the characteristics of the monthly variations of mass concentrations of air pollutants were determined. We used the Pearson correlation coefficient to establish the relationship between PM2.5, PM10, and the gas pollutants. The results revealed significant differences in the concentration levels of air pollutants and in the variations between the different cities. The Pearson correlation coefficients between PMs and NO2 and SO2 were either high or moderate (PM2.5 with NO2: r=0.256–0.688, mean r=0.498; PM10 with NO2: r=0.169–0.713, mean r=0.493; PM2.5 with SO2: r=0.232–0.693, mean r=0.449; PM10 with SO2: r=0.131–0.669, mean r=0.403). The correlation between PMs and CO was diverse (PM2.5: r=0.156–0.721, mean r=0.437; PM10: r=0.06–0.67, mean r=0.380). The correlation between PMs and O3 was either weak or uncorrelated (PM2.5: r=−0.35 to 0.089, mean r=−0.164; PM10: r=−0.279 to 0.078, mean r=−0.127), except in Haikou (PM2.5: r=0.500; PM10: r=0.509).
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