Air pollution scenario over Pakistan: Characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases

Bilal, Muhammad; Mhawish, Alaa; Nichol, Janet Elizabeth; Qiu, Zhongfeng; Nazeer, Majid; Ali, Arfan; Leeuw, G. de; Levy, Robert C.; Wang, Yu; Yang, Chen; Wang, Lunche; Shi, Yuan; Bleiweiss, Max P.; Mazhar, Usman; Atique, Luqman; Song, Ke

doi:10.1016/j.rse.2021.112617

Cited by 96 publications

(41 citation statements)

References 149 publications

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“…In terms of seasonality, PM 2.5 , PM 10 , SO 2 , NO 2 , and CO experienced the same seasonal variation, e.g., highest in winter and lowest in summer. Higher pollution in winter is associated with increased coal combustion, civil heating, power generation, fossil fuel burning, industrial activity, vehicular exhausts, and adverse/stagnant meteorology [13][14][15][26][27][28][29][30][50][51][52][71][72][73][74][75]. In the case of PM 10 , higher pollution levels also occurred from March to May (spring) due to haze events [76,77].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

Zaib

Bilal

2022

Atmosphere

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In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants’ (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China.

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Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

Zaib

Bilal

2022

Atmosphere

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“…The spatial resolution of the aerosol optical depth at a 550 nm forecast used in the present study was 0.4 • × 0.4 • (spatial resolution of the CAMS forecasts), whereas the temporal resolution of CAMS reanalysis is 3 hourly. The use of the CAMS AOD and other details are described in several earlier papers, and are hence not repeated here [36,42,[46][47][48][49].…”

Section: Aerosol Modellingmentioning

confidence: 99%

Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India?

et al. 2022

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The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m2 and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m2 for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.

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“…The OMI sensor uses an algorithm of OMAERUV to retrieve absorbing aerosol optical depth (AAOD, 388 nm), the ultraviolet aerosol index (UVAI), the AOD, and the SSA [54][55][56]. This sensor also provides atmospheric trace gases (e.g., O 3 , NO 2 , and SO 2 ) [21,[57][58][59][60][61][62]. In this study, long-term (2005−2020) OMAERUV version 3, level 3 daily cloud-screened (cloud fraction < 30%) total column NO 2 (OMNO2d) and SO 2 (cloud radiance fraction < 0.2, OMSO2e) products at a spatial resolution of 0.25 • × 0.25 • were used.…”

Section: Omi Datamentioning

confidence: 99%

“…Fossil fuel combustion, industrial emissions, automobile emissions, biomass burning, natural lightning, and soil microbe emissions are the main sources of NO 2 [16,17,62,72]. In contrast, volcanoes, coal, oil and gas, and smelters are the major contributors to anthropogenic SO 2 emissions [72].…”

Section: Spatial Distributions Of No 2 and Somentioning

confidence: 99%

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Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

et al. 2021

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Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

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Air pollution scenario over Pakistan: Characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases

Cited by 96 publications

References 149 publications

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India?

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

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