Mercury,Trace Elements and Organic Constituents in Atmospheric Fine Particulate Matter,Shenandoah National Park,Virginia, USA: A Combined Approach to Sampling and Analysis

Compliance with U.S. air quality regulatory standards for atmospheric fine particulate matter (PM_2.5) is based on meeting average 24 hour (35 μ m^?3) and yearly (15 μg m^?3) mass‐per‐unit‐volume limits, regardless of PM_2.5 composition. Whereas this presents a workable regulatory framework, information on particle composition is needed to assess the fate and transport of PM_2.5 and determine potential environmental/human health impacts. To address these important non‐regulatory issues an integrated approach is generally used that includes (1) field sampling of atmospheric particulate matter on filter media, using a size‐limiting cyclone, or with no particle‐size limitation; and (2) chemical extraction of exposed filters and analysis of separate particulate‐bound fractions for total mercury, trace elements and organic constituents, utilising different USGS laboratories optimised for quantitative analysis of these substances. This combination of sampling and analysis allowed for a more detailed interpretation of PM_2.5 sources and potential effects, compared to measurements of PM_2.5 abundance alone. Results obtained using this combined approach are presented for a 2006 air sampling campaign in Shenandoah National Park (Virginia, USA) to assess sources of atmospheric contaminants and their potential impact on air quality in the Park. PM_2.5 was collected at two sampling sites (Big Meadows and Pinnacles) separated by 13.6 km. At both sites, element concentrations in PM₂₅ were low, consistent with remote or rural locations. However, element/Zr crustal abundance enrichment factors greater than 10, indicating anthropogenic input, were found for Hg, Se, S, Sb, Cd, Pb, Mo, Zn and Cu, listed in decreasing order of enrichment. Principal component analysis showed that four element associations accounted for 84% of the PM_2.5 trace element variation; these associations are interpreted to represent: (1) crustal sources (Al, REE); (2) coal combustion (Se, Sb), (3) metal production and/or mobile sources (Mo, Cd, Pb, Cu, Zn) and (4) a transient marine source (Sr, Mg). Concentrations of Hg in PM_2.5 at background levels in the single pg m^?3 were shown by collection and analysis of PM_2.5 on filters and by an automated speciation analyser set up at the Big Meadows air quality site. The speciation unit revealed periodic elevation of reactive gaseous mercury (RGM) that co‐occurred with peaks in SO₂, indicating an anthropogenic source. GC/MS total ion current chromatograms for the two sites were quite similar indicating that organic signatures were regional in extent and/or that the same compounds were present locally at each site. Calculated carbon preference index values for n‐alkanes indicated that plant waxes rather than anthropogenic sources, were the dominant alkane source. Polycyclic aromatic hydrocarbons (PAHs) were detected, with a predominance of non‐alkylated, and higher molecular weight PAHs in this fraction, suggestive of a combustion source (fossil fuel or forest fires).     

Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013–2017

Based on long-term PM_2.5 data observed at high temporal and spatial resolution, the relationships between PM_2.5, primary emission, and weather factors in China during four seasons were examined using statistical analysis. The results reveal that primary emission plays a decisive role in the spatial distribution and seasonal variability of PM_2.5, except in western China, where PM_2.5 is controlled by dust weather. In addition to the accumulation of primary emissions, unfavorable meteorological conditions for the diffusion of air pollution lead to the occurrence of PM_2.5 pollution. The significant dynamic factors affecting PM_2.5 concentration are surface wind speed, planet boundary layer height, and ventilation coefficient, especially in winter. The ventilation coefficient is inversely correlated with PM_2.5. Better ventilation is more favorable for the dilution and outflow of local PM_2.5. However, in spring and autumn, ventilation coefficient and PM_2.5 are positively correlated over the southern regions with low emission, indicating that ventilation also affects the inflow of PM_2.5 from outside the region. Wind shear, 850 hPa divergence, and vertical velocity have insignificant effects on the long-term variations in PM_2.5. The significant thermal factor is 850 hPa temperature in winter, except in the Pearl River Delta and Xinjiang regions. In spring, the influence of each thermal factor is weak. In summer, the influences of temperature and humidity are more significant than in spring. In autumn, the influence of humidity is relatively obvious, compared with other thermal factors. The correlation coefficients between multi-factors regressed and observed PM_2.5 concentrations pass the 95% confidence test, and are higher than that of single-factor regression over most regions. The observed data from December 2016 to February 2017 were chosen to test the regression equation. The test result reveals that the regression equation is effective for predicting PM_2.5 concentrations over regions with high primary emission.     

Experimental and numerical analyses of particulate matter concentrations in underground subway station

The purpose of this paper was to perform the experimental and numerical analyses of PM₁₀ and PM_2.5 concentrations in Imam Khomeini (IKH) underground subway station in Tehran. The aim was to provide fundamental data in order to fulfill workers and passengers respiratory health necessities. Experimental measurements was done at three different locations (entrance, middle and exit) inside the platform and also outdoor ambient of the station. The Dust-Trak was applied to measure continuous PM_2.5 and PM₁₀ concentrations at a logging interval of 30 s. The measurements were recorded during rush hours (8:00 am–12:00 pm) for one week per each season from June 2015–June 2016.Moreover, computational fluid dynamic (CFD) simulation was done for the platform of the above station and the necessary boundary conditions were provided through field measurements. Those basic parameters which were considered for numerical analysis of particulate matters concentrations included air velocity, air pressure and turbulence. Furthermore, the piston effect caused by train movement inside the station provided natural ventilation in the platform. The results showed that seasonal measured PM_2.5 and PM₁₀ indoor concentrations had a variety range from 40–98 µg/m³ to 33–102 µg/m³, respectively, and were much higher than national indoor air quality standard levels. Meanwhile, PM_2.5 and PM₁₀ concentrations in the IKH underground subway station were approximately 2.5–2.9 times higher than those in outdoor ambient, respectively. Numerical simulation indicated that the predicted concentrations were underestimated by a factor of 8% in comparison with the measured ones.     

Assessment of air quality during 19th Common Wealth Games at Delhi, India

The 19th Common Wealth Games was organized at Delhi, India, during October 3 to 14, 2010, where more than 8,000 athletes from 71 Commonwealth Nations have participated. In order to give them better environment information for proper preparedness, mass concentrations of particulate matters below 10 microns (PM₁₀) and 2.5 microns (PM_2.5), black carbon (BC) particles and gaseous pollutants such as carbon monoxide (CO) and nitrogen oxide (NO) were monitored and displayed online for ten different locations around Delhi, including inside and outside the stadiums. This extensive information system for air quality has been set up for the period from September 24 to October 21, 2010, and data have been archived at 5-min interval for further research. During the study period, average concentration of PM₁₀ and PM_2.5 was observed to be 229.7 ± 85.5 and 112.1 ± 56.0 μg m^?3, respectively, which is far in excess of the corresponding annual averages, stipulated by the national ambient air quality standards. Significant large and positive correlation (r = 0.93) between PM₁₀ and PM_2.5 implies that variations in PM₁₀ mass are governed by the variations in PM_2.5 mass. The mass concentrations of PM_2.5 inside the stadium were found to be ~18 % lower than those outside; however, no large variations were observed in PM₁₀. Mean concentrations of BC, CO and NO for the observation period were 10.9 μg m^?3 (Min, 02 μg m^?3; Max, 31 μg m^?3), 1.83 ± 0.89 ppm (Min, 0.48 ppm; Max, 4.55 ppm) and 37.82 ppb (Min, 2.4 ppb; Max, 206.05 ppb), respectively. BC showed positive correlation (r = 0.73) with CO suggests unified source for both of them, mainly from combustion emissions. All the measured parameters, however, show a significant diurnal variation with enhanced peaks in the morning and late night hours and lower values during daytime.     

Spatial heterogeneity in near surface aerosol characteristics across the Brahmaputra valley

In order to examine the spatial variability of the aerosol characteristics across the Brahmaputra valley, a land campaign was conducted during late winter (February 3–March 2) 2011. Measurements of particulate matter (PM, PM₁₀, PM_2.5) and black carbon (BC) concentrations were made onboard an interior redesigned vehicle. The length of the campaign trail stretched about 700 km, covering the longitude belt of 89.97°–95.55°E and latitude belt of 26.1°–27.6°N, comprising 13 measurement locations. The valley is divided into three sectors longitudinally: western sector (R1: 89.97°–91.75°E), middle sector (R2: 92.5°–94.01°E) and eastern sector (R3: 94.63°–95.55°E). Spatial heterogeneity in aerosol distribution has been observed with higher PM₁₀ and PM_2.5 concentrations at the western and middle sectors compared to the eastern sector. The locations in the western sector are found to be rich in BC compared to the other two sectors and there is a gradual decrease in BC concentrations from west to east of the Brahmaputra valley. Two hotspots within the western and middle sectors with high PM and BC concentrations have been identified. The associated physico-optical parameters of PM reveal abundance of PM_2.5 aerosols along the entire valley. High population density in the western and middle sectors, together with the contribution of remote aerosols, leads to higher anthropogenic aerosols over those regions. Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) slightly underestimates the measured PM₁₀ and PM_2.5 at the eastern sector while the model overestimates the measurements at a number of locations in the western sector. In general, BC is underestimated by the model. The variation of BC within the campaign trail has not been adequately captured by the model leading to higher variance in the western locations as compared to the middle and eastern locations.     

Assessment of spatial variations of particulate matter (PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript>) in Bahrain identified by air quality index (AQI)

The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM₁₀ and PM_2.5), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM₁₀ and PM_2.5 are the most critical air pollutants in Bahrain with PM_2.5 prevailing during January 2012 and PM₁₀ prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM₁₀ concentrations were observed in the Northern Governorate while the highest PM_2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM_2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM_2.5/PM₁₀ ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM₁₀ and PM_2.5 in comparison to August 2012.     

A new method for measurement of air change rate based on indoor PM<Subscript>2.5</Subscript> removal

Outdoor PM_2.5 easily flows into indoor and seriously influences indoor air quality due to its characteristics of flow, diffusion and penetration. It is a proper ‘gas’ tracer similar to CO₂ to study building ventilation. Therefore, in this paper, a model for calculating air change rates by removing indoor PM_2.5 was deduced. Also, some factors influencing the air change rate were qualitatively analyzed and the expression of possible air change rate error was given. The comparison between the results from PM_2.5 removal method and the data from CO₂ decay method validated the model. The relative error between the results of the two methods is less than 10%. On the basis of validating the model, this paper presented the research of air change rates in ten naturally ventilated house rooms in three Chinese cities. It is found that the rooms with the ventilation rates of 1.15–6.75 m³/h/person have inadequate ventilation.     

Temporal analysis of air pollution and its relationship with meteorological parameters in Bahrain, 2006–2012

The objective of this paper is to analyze temporal and seasonal trends of air pollution in Bahrain between 2006 and 2012 by utilizing datasets from five air quality monitoring stations. The non-parametric and robust Theil-Sen approach is employed to study quantitatively temporal variations of particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃). The calculated annual concentrations for PM₁₀ and PM_2.5 in Bahrain were substantially higher than recommended World Health Organization (WHO) guideline standards. Results showed increasing trends for PM₁₀, PM_2.5, and SO₂ whereas O₃ and its precursor NO₂ showed decreasing behavior. The general increase in air pollution trends is in agreement with prediction of air pollution models for Middle East region due to economic growth, industrialization, and urbanization. The significances of long-term trends were examined. Additional to actual (unadjusted) trends, meteorological adjusted (deseasonalized) trends and seasonal trends were quantified. The box-plot analysis visually illustrated monthly variations of key air pollutants. It showed that only PM₁₀ and PM_2.5 exhibited seasonal pattern, and their concentrations increased during summer and decreased during winter. The effects of ambient air temperature, relative humidity, wind speed, and rainfall on particulate matter (PM) concentrations were further investigated. The Spearman correlation coefficient results demonstrated significant negative correlation between relative humidity and PM concentrations (??0.595 for PM₁₀ and ??0.526 for PM_2.5) while significant positive correlation was observed between temperature and PM concentrations (0.420 for PM₁₀ and 0.482 for PM_2.5).     

Air quality and elemental enrichment factors of aerosol particulate matter in Riyadh City, Saudi Arabia

Air particulate matter (PM) samples were collected from June 2006 to May 2007 for determination of chemical elements. PM samples were taken in two size fractions (PM_2.5 and PM₁₀) with MiniVolume air samplers on rooftops of various buildings (15–25 m above ground) in the city of Riyadh. The samples were subjected to X-ray fluorescence analysis to measure major (Na, Mg, Al, K, Ca, Si, P, S, and Fe) and trace elements (Mn, Ni, Cu, Zn, and Ba). The results showed that the PM concentrations were higher for PM₁₀ compared to PM_2.5, indicating that the major PM source was local dust. Also the spatial distribution with high PM concentrations was observed in the south and southeast of the city and the lowest levels were in the center and northeast of the city. This spatial distribution was attributed to different factors such as wind direction and velocity, emission from cement factories, and the presence of buildings, trees, and paved streets that reduce the amount of dust resuspended into the atmosphere. The air quality of the city was found to range from good to hazardous based on PM_2.5, and from good to very hazardous based on PM₁₀. The element-enrichment factors revealed two element groups according to their changing spatial behavior. The first group showed no significant spatial changes indicating they have the same common source. The second group (mainly S and Ni) exhibited significant changes as expected from anthropogenic inputs. The origin of S is possibly a combination of minerals (CaSO₄) and fossil fuel combustion. The source of Ni is probably from fossil fuel combustion.     

The characteristics of air pollutants during different seasons in the urban area of Lanzhou,Northwest China

Based on data from ground-based air quality stations, space–time variations of six principal atmospheric pollutants, such as particulate matter (PM_2.5 and PM₁₀) and gas pollutants (SO₂, NO₂, СО, and O₃), obtained from January 1, 2014 to December 31, 2017 in the city of Lanzhou, have been studied. Average total concentrations of PM_2.5 and PM₁₀ were 53.2?±?26.91 and 124.54?±?82.33 µg/m³, respectively; however, the results showed that in 75.53% and 84.85% days, concentrations of these pollutants exceeded Chinese National Ambient Air Quality Standard and in 100% days exceeded World Health Organization guidelines standards. Daily mean values of aerosol optical depth and Ångström exponent based on data, received by satellite Moderate Resolution Imaging Spectroradiometer, show a broad range of values for aerosol optical depth (from 0.018 to 1.954) and Ångström exponent (from 0.003 to 1.8). Results of principal components analysis revealed three factor loadings. Thus, Factor 1 has the relevant loadings for PM_2.5, PM₁₀, CO, SO₂, and NO₂ (36%) and closely associated with transport emissions and industrial sources, which contribute to air pollution in Lanzhou. Factor 2 was heavily loaded with temperature and visibility (16.94%). Factor 3 consisted of relative humidity (14.11%). Cluster analysis revealed four subgroups: cluster 1 (PM_2.5, NO₂, SO₂), cluster 2 (CO), cluster 3 (PM₁₀) and cluster 4 (relative humidity, visibility, temperature, O₃, wind speed), which were compliant with results, obtained from principal components analysis. Positive correlation was found among all pollutants, other than O₃. According to processed backward trajectories obtained by Hybrid Single-Particle Lagrangian Integrated Trajectory model, it was found that movement of air masses occur from north, northwest, and west directions—the location of principal natural sources of aerosols.     

Lockdown during COVID-19 pandemic: A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PM_2.5 and NO₂ concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM_2.5 and NO₂ concentration in urban cities, causing low concentrations. It is observed that the original PM_2.5 and NO₂ concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM_2.5 and NO₂ concentrations. Hurst exponent of the PM_2.5 and NO₂ concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.     

High resolution aerosol data from MODIS satellite for urban air quality studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM_2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R² =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM_2.5 ground concentrations. Finally, we studied the relationship between PM_2.5 and AOD at the intra-urban scale (?10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM_2.5 relationship does not depend on relative humidity and air temperatures below ～7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM_2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM_2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM_2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R² of 0.86). Therefore, adjustment for the daily variability in the AOD-PM_2.5 relationship provides a means for obtaining spatially-resolved PM_2.5 concentrations.     

Ecological model analysis of respiratory health risk factors by ambient air pollution in Lucknow,the capital City of Uttar Pradesh,India

Ambient air pollution, particularly in the urban environment of developing countries, has turned out to be a major health risk factor. We explore the compounded impact of age sensitivity, exposure, poverty, co-morbidity, etc., along with composite air pollution in determining morbidity and health burden of people in Lucknow, India. This cross-sectional study is confined to analyse respiratory health status across different socio-economic and geographic locations using n = 140 in-depth questionnaire method. We used mean daily ambient air pollution data of PM₁₀, PM_2.5, SO_2, and NO₂ for the 2008–2018 period. We used the ecological model framework to assess the risk at different hierarchical levels and compounded severity on a spatial scale. We also used Logistic regression model with log odds and odds ratio to analyze the association of risks outcomes with composite air pollution scores calculated using the principal component analysis method. There is a strong association of location-specific respiratory disease prevalence with an overall 32 percent prevalence. The prevalence of ecological model 1 (individual domain) is 4.3 percent, while ecological model 2 (community domain) has the highest prevalence at 32.4 percent. The logistic regression model shows that respiratory disease load is positively associated with age sensitivity (P < .001) and composite pollution level (P < .001). For another model with suffocation as the outcome variable, composite pollution level (P < .001) and exposure (P < .001) are positively associated. Optimum interventions are required at Ecological models 1, 2, and 3 levels for better respiratory health outcomes.

     

Particulate air pollution in a small settlement: The effect of local heating

Mass concentrations of PM₁₀, PM_2.5, and black smoke (BS) were measured in April 2003 during a 3-week campaign in a small village and at a nearby background location in the central part of the Czech Republic. In a pilot analysis, concentrations of selected trace elements (Al, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Sb, Cs, Pb) in the collected aerosol were determined by means of ICP-MS. Average concentrations of both PM fractions and BS were higher in the village (37, 26 and 26 μg m⁻³) than at the background location (26, 19 and 11 μg m⁻³) for PM₁₀, PM_2.5 and BS, respectively. Both PM₁₀ and PM_2.5 were reasonably correlated in the village (r = 0.80) and also at the background location (r = 0.79). Correlation between same fractions from the village and from the background site were even higher (r = 0.97 and r = 0.95 for the PM₁₀ and PM_2.5, respectively) suggesting that most of the aerosol in both locations may be influenced by similar sources. The ratio between PM₁₀ and PM_2.5 showed that sources in the village contributed about 33% and 35% to local aerosol concentration for PM₁₀ and PM_2.5, respectively. When the data from the two rural locations were compared with corresponding 24-h averages of PM₁₀ concentrations obtained for the period of the campaign from fixed site monitors situated near larger towns, the highest concentration was found in Prague the Czech capital (49 μg m⁻³) followed by a district town Beroun (41 μg m⁻³) and the village (37 μg m⁻³). The lowest PM₁₀ concentration was found in the village background (26 μg m⁻³). Elemental analysis revealed higher concentrations for most of the elements characteristic of combustion aerosol (namely Zn, Pb, As, Mn and Ti) in the PM collected in the village. The results support the idea that traditional heating in villages may contribute a great extent to local air pollution and may represent an important problem.     

Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network

China has currently entered a critical stage of coordinated control of fine particulate matter (PM_2.5) and ozone (O₃), it is thus of tremendous value to accurately acquire high-resolution PM_2.5 and O₃ data. In contrast to traditional studies that usually separately estimate PM_2.5 and O₃, this study proposes a knowledge-informed neural network model for their joint estimation, in which satellite observations, reanalysis data, and ground station measurements are used. The neural network architecture is designed with the shared and specific inputs, the PM_2.5-O₃ interaction module, and the weighted loss function, which introduce the prior knowledge of PM_2.5 and O₃ into neural network modeling. Cross-validation (CV) results indicate that the inclusion of prior knowledge can improve the estimation accuracy, with R² increasing from 0.872 to 0.911 and from 0.906 to 0.937 for PM_2.5 and O₃ estimation under sample-based CV, respectively. In addition, the proposed joint estimation model achieves comparable performance with the separate estimation model, but with higher efficiency. Mapping results of PM_2.5 and O₃ derived by the proposed model have demonstrated interesting findings in the spatial and temporal trends and variations over China.     

COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India

COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020 (first phase), extended up to 3rd May 2020 (second phase), and further extended up to 17th May 2020 (third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters (PM) i.e., PM₁₀, PM_2.5, carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ammonia (NH₃) and ozone (O₃), and associated temperature fluctuation in four megacities (Delhi, Mumbai, Kolkata, and Chennai) from different regions of India were investigated. In this pandemic period, air temperature of Delhi, Kolkata, Mumbai and Chennai has decreased about 3 °C, 2.5 °C, 2 °C and 2 °C respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration (?41.91%, ?37.13%, ?54.94% and ?46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PM_2.5 has experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.     

Forecasting hourly particulate matter concentrations based on the advanced multivariate methods

In this study, several multivariate methods were used for forecasting hourly PM₁₀ concentrations at four locations based on SO₂ and meteorological data from the previous period. According to the results, boosted decision trees and multi-layer perceptrons yielded the best predictions. The forecasting performances were similar for all examined locations, despite the additional PM₁₀ spatio-temporal analysis showed that the sites were affected by different emission sources, topographic and microclimatic conditions. The best prediction of PM₁₀ concentrations was obtained for industrial sites, probably due to the simplicity and regularity of dominant pollutant emissions on a daily basis. Conversely, somewhat weaker forecast accuracy was achieved at urban canyon avenue, which can be attributed to the specific urban morphology and most diverse emission sources. In conclusion to this, the integration of advanced multivariate methods in air quality forecasting systems could enhance accuracy and provide the basis for efficient decision-making in environmental regulatory management.     

Changes in PM<Subscript>10</Subscript> concentration due to large-scale rainfall

This paper is concerned with the estimation of the removal efficiency of PM₁₀ by large-scale precipitation under no-wind conditions in a background (rural) and urban areas. The changes in PM₁₀ concentrations before, during and after the presence of rainfall were studied from 2007 to 2013. The study was conducted in two different locations identified with regard to air quality. DAVIS weather stations were used to determine the meteorological conditions. The concentration of PM₁₀ was calculated with the use of the gravimetric reference method. Two hundred and ninety-nine measurement series were carried out. A linear relationship was found between the intensity and duration of rainfall and the value of the removal coefficient (ΔC). It was proved that except light rains, for the near-to-ground troposphere, the effectiveness of the removal of PM₁₀ (ΔC) did not assume different values at various locations for rainfall with the same intensity and duration. It was found that a temporary interaction of the effect of the purification by wet deposition was being minimised in areas characterised by low air quality. It was confirmed that intense rains resulted in the maintenance of higher values of air quality.     

中国北方典型中小盆地城市大气颗粒物污染特征及趋势对比

分析了2014~2018年北方5个典型中小盆地城市(兰州、银川、临汾、太原、南阳)PM10与PM2.5的浓度变化特征和大致来源类型。除2018年银川PM2.5浓度外,各市PM10和PM2.5年均浓度均超标;兰州、银川和南阳PM10与PM2.5呈逐年下降趋势,南阳下降最明显;临汾PM10与PM2.5呈逐年上升趋势;太原PM10与PM2.5稳定维持在一个高浓度状态。5个城市颗粒物浓度的季节变化特征一致:冬春高、夏秋低。对PM2.5/PM10值而言,冬季和夏季该比值较高,分别受取暖和降水的影响;春季和秋季该比值较低,分别受沙尘和秸秆焚烧及高强度建筑施工的影响。5市PM2.5和PM10浓度具有良好的线性关系,细颗粒占比大小顺序为临汾>南阳>太原>银川>兰州。     

Coarse and fine particulates, Hg(p)-bound particulate concentrations and compositions study at Sha-Lu, central Taiwan

Ambient air and coarse, fine and particulate-bound mercury (Hg(p)) pollutants were collected and analyzed from March 17 to May 22 and September 3, 2009 to March 5, 2010 at a highway traffic site located in Sha-Lu, central Taiwan. This study has the following objectives: (1) to measure the coarse and fine particulates concentrations and the particulate-bound mercury Hg(p) which was attached to these particulate; (2) to determine the average Hg(p) compositions in coarse and fine particulates and (3) to compare the Hg(p) concentrations and compositions particulate in this study to the those obtained in other studies. The results obtained in this study indicated that the average ambient air PM_2.5, PM_2.5–10 and PM₁₀ were 18.79 ± 6.71, 11.22 ± 4.93 and 30.01 ± 10.27 μg/m³, respectively. The ranges of concentrations for Hg(p) in PM_2.5 were from 0.0016 to 0.0557 ng/m³, from 0.0006 to 0.0364 ng/m³ in PM_2.5–10 and from 0.0022 to 0.0862 ng/m³ in PM₁₀. In addition, the highest particle-bound mercury compositions in PM_2.5 were 16.85 ng/g and the lowest particle-bound mercury concentrations were 0.55 ng/g. The highest particle-bound mercury compositions in PM_2.5–10 were 13.88 ng/g and the lowest particle-bound mercury in PM_2.5–10 were 0.22 ng/g.