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Air quality impairment has a massive impact on human health, with atmospheric particulate matter (PM) playing a major role. The People's Republic of China experienced a trend of increasing PM2.5 concentrations from 2000 to 2013. However, after the application of the Air Pollution Prevention and Control Action Plan and other related control measures, sharp decreases in air pollutant concentrations were particularly evident in the city of Wuhan (central China). This study analysed major changes in PM2.5 concentrations, composition and source apportionment (using receptor modelling) based on Wuhan's PM2.5 chemical speciation datasets from 2006 to 2007, 2019-2021 and contemporaneous gaseous pollutant values. Average SO2 concentrations decreased by 88%, from the first to the second period, mostly due to measures that reduced coal combustion. However, NO2 only declined by 25%, with policy measures likely being undermined by an increased number of vehicles. PM2.5 concentrations decreased by 65%, with the PM constituents each being affected differently. Coal combustion-related element concentrations, OC, SO42-, NH4+, EC, Cl-, Al, Ca, Cu, Fe, Co and NO3- decreased by 22-90%. Secondary inorganic aerosol (SIA) was initially dominated by (NH4)2SO4 (73%) in 2006, but later dominated by NH4NO3 (52%) in 2021. Receptor modelling identified major sources contributing to PM2.5: Mineral, road and desert dust (MRDD), Secondary sulphate (SECS), Secondary nitrate (SECN), Tungsten industry (W), Toxic Elements of Coal (TEC), Iron and Steel (IRONS), Coal Combustion (CC), Residential Heating (RH), Refinery (REF) and Traffic (TRF). In relative proportions, TEC (-83%), SECS (-64%) and SECN (-48%) reduced their contributions to PM2.5 whilst MRDD increased (+62.5%). Thus, the results indicate not only a drastic abatement of PM pollution in Wuhan but also a change in the sources of pollution, which requires further actions to reduce PM2.5 concentrations to health protective values. Secondary PM a

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs va

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs va

Samples of rainwater (RW) were collected to characterize the chemistry and sources in two representative megacities at Pune (Southwest) and Delhi (Northern) India from 2011 to 2014 across two seasons: monsoon (MN) and non-monsoon (NMN). Collected RW samples were analyzed for major chemical constituents (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Ca2+, and Mg2+), pH and conductivity. In addition, bicarbonate (HCO3-) was also estimated. The mean pH values of the RW were >6 at Pune and <6 at Delhi and 4% and 26% were acidic, respectively. The mean sum of all measured ionic species in Pune and Delhi was 304.7 and 536.4 mu ep/l, respectively, indicating that significant atmospheric pollution effects in these Indian mega cities. Both the Ca2+ and SO42- were the dominant ions, accounting for 43% (Pune) and 54% (Delhi) of the total ions. The sum of measured ions during the NMN period was greater than the NM period by a factor of 1.5 for Pune (278.4: NM and 412.1: NMN mu eq/l) and a factor of about 2.5 for Delhi (406 and 1037.7 mu eq/l). The contributions of SO42- and NO3- to the RW acidity were similar to 40% and 60%, respectively, at Pune and correspondingly, 36% and 64% at Delhi. The concentrations of secondary aerosols (SO42- and NO3-) were higher by a factor of two and three when the air masses were transported to Pune from the continental side. At Delhi, the concentrations of SO42-, NO3-, Ca2+, and Mg2+ were significantly higher when the air masses arrive from Punjab, Haryana, and Pakistan indicating the greater atmospheric pollution over the Indo-Gangetic Plain. Positive matrix factorization was applied to the source apportionment of the deposition fluxes of these ions. Three factors were obtained for Pune and four for Delhi. The sources at Pune were secondary aerosols from fossil fuel combustion, soil dust, and marine, whereas, at Delhi, the sources were soil, fossil fuel combustion, biomass burning, and industrial chlorine.

The ground and vertical profiles of particulate matter (PM) were mapped as part of a pilot study using a Tethered balloon within the lower troposphere (1000 m) during the foggy episodes in the winter season of 2015-16 in New Delhi, India. Measurements of black carbon (BC) aerosol and PM <2.5 and 10 mu m (PM2.5 &PM-10 respectively) concentrations and their associated particulate optical properties along with meteorological parameters were made. The mean concentrations of PM2.5, PM10, BC370 (nm), and BC880 nm were observed to be 146.8 +/- 42.1, 245.4 +/- 65.4, 30.3 +/- 122, and 24.1 +/- 103 mu g m(-3), respectively. The mean value of PM2.5 was similar to 12 times higher than the annual US-EPA air quality standard. The fraction of BC in PM2.5 that contributed to absorption in the shorter visible wavelengths (BC370 nm) was-21%. Compared to clear days, the ground level mass concentrations of PM2.5 and BC370 nm particles were substantially increased (59% and 24%, respectively) during the foggy episode. The aerosol light extinction coefficient (sigma(ext)) value was much higher (mean: 610 Mm(-1)) during the lower visibility (foggy) condition. Higher concentrations of PM2.5 (89 mu g m(-3)) and longer visible wavelength absorbing BC880 am (25.7 mu g m(-3)) particles were observed up to 200 m. The BC880 nm and PM2.5 aerosol concentrations near boundary layer (1 km) were significantly higher (similar to 1.9 and 12 mu g m(-3)), respectively. The BC (i.e BCtot) aerosol direct radiative forcing (DRF) values were estimated at the top of the atmosphere (TOA), surface (SFC), and atmosphere (ATM) and its resultant forcing were- 75.5 Wm(-2) at SFC indicating the cooling effect at the surface. A positive value (20.9 Wm(-2)) of BC aerosol DRF at TOA indicated the warming effect at the top of the atmosphere over the study region. The net DRF value due to BC aerosol was positive (96.4 Wm(-2)) indicating a net warming effect in the atmosphere. The contribution of fossil and biomass

Black carbon (BC) aerosols are one of the most uncertain drivers of global climate change. The prevailing view is that BC mass concentrations are low in rural areas where industrialization and vehicular emissions are at a minimum. As part of a national research program called the Ganga Basin Ground Based Experiment-2014 under the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) Phase-III of Ministry of Earth Sciences, Government of India, the continuous measurements of BC and particulate matter (PM) mass concentrations, were conducted in a rural environment in the highly-polluted Indo-Gangetic Plain region during 16th June to 15th August (monsoon period), 2014. The mean mass concentration of BC was 4.03 (+/- 0.85) mu g m(-3) with a daily variability between 2.4 and 5.64 mu g m(-3), however, the mean mass PM concentrations [near ultrafine (PM1.0), fine (PM2.5) and inhalable (PM1.0)] were 29.1(+/- 16.2), 34.7 (+/- 19.9) and 43.7 (+/- 283) mu g m(-3), respectively. The contribution of BC in PM1.0 was approximately 13%, which is one of the highest being recorded. Diurnally, the BC mass concentrations were highest (mean: 5.89 mu g m(-3)) between 20:00 to 22:00 local time (LT) due to the burning of biofuels/biomass such as wood, dung, straw and crop residue mixed with dung by the local residents for cooking purposes. The atmospheric direct radiative forcing values due to the composite and BC aerosols were determined to be +78.3, +44.9, and +45.0 W m(-2) and +42.2, +35.4 and +34.3 W m(-2) during the months ofJune, July and August, respectively. The corresponding atmospheric heating rates (AHR) for composite and BC aerosols were 2.21,1.26 and 1.26; and 1.19, 0.99 and 0.96 K day(-1) for the month ofJune, July and August, respectively, with a mean of 1.57 and 1.05 K day(-1) which was 33% lower AHR (BC) than for the composite particles during the study period. This high AHR underscores the importance of absorbing aerosols such as BC contributed by r

In the Sundsvall Bay area, located in the southern Bothnian Sea (a sub-basin of the Baltic Sea), industrial history have included a large variety of activities, from small sawmills to large pulp and paper mills as well as chloralkali production. Previously, several sawmills in the area used chlorophenol formulations for wood preservation, and these were contaminated by PCDD/Fs. Chloralkali production using graphite electrodes and bleaching of pulp using chlorine are manufacturing processes also known to have caused PCDD/Fs emissions. A chemical industry has been active in the area since the 1930s and an aluminum smelter was built in 1942. Aluminum production, like many high temperature processes, is known to produce PCDD/Fs. The aim of this study was to investigate the possibility of using receptor modeling for identification and apportioning of the major sources of PCDD/Fs in the Sundsvall Bay. The Sundsvall Bay was chosen as the first study area of this modeling technique along the Swedish coast since there are several possible sources of PCDD/Fs in the area and a relatively large number of sediment samples have been analyzed. Current PCDD/F emissions have been investigated by sampling river water, industrial effluent entering the bay and, in addition, soil from two contaminated sawmill sites.