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Latha R, Mukherjee A, Dahiya K, Bano S, Pawar P, Kalbande R, Maji S, Beig G, Murthy BS. On the varied emission fingerprints of particulate matter over typical locations of NCR (Delhi) - A perspective for mitigation plans. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 311:114834. [PMID: 35287076 DOI: 10.1016/j.jenvman.2022.114834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 03/01/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Source apportionment study of PM2.5 using positive matrix factorization was performed to identify the emission characteristic from different sectors (sub-urban residential, industrial and rapidly urbanizing) of Delhi during winter. Chemical characterization of PM2.5 included metals (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb and Zn), water soluble ionic compounds (WSICs) (Cl-, NO3-, SO42- and NH4+) and Carbon partitions (OC, EC). Particulates (PM2.5) were collected on filter twice daily for stable and unstable atmospheric conditions, at the locations with specific characteristics, viz. Ayanagar, Noida and Okhla. Ions solely occupied 50% of the total PM2.5 concentration. Irrespective of location, high correlation between OC and EC (0.871-0.891) at p ≤ 0.1 is observed. Relatively lower ratio of NO3/SO4 at Ayanagar (0.696) and Okhla (0.84) denotes predominance of emission from stationary sources rather than mobile sources like that observed at Noida (1.038). Using EPA PMF5.0, optimum factors for each location are fixed based on error estimation (EE). Crustal dust, vehicular emission, biomass burning and secondary aerosol are the major contributing sources in all the three locations. Incineration contributes about 19% at Ayanagar and 18% at Okhla. Metal industries in Okhla contribute about 19% to PM2.5. These specific local emissions with considerable potency are to be targeted for long-term policymaking. Considerable secondary aerosol contribution (15%-24%) indicates that gaseous emissions also need to be reduced to improve air quality.
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Affiliation(s)
- R Latha
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - A Mukherjee
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - K Dahiya
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - S Bano
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - P Pawar
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - R Kalbande
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - S Maji
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - G Beig
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - B S Murthy
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
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Rajput JS, Trivedi MK. Determination and assessment of elemental concentration in the atmospheric particulate matter: a comprehensive review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:243. [PMID: 35243563 DOI: 10.1007/s10661-022-09833-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 01/29/2022] [Indexed: 06/14/2023]
Abstract
The elemental concentrations of atmospheric particulate matter (PM) have a detrimental effect on human health in which some elemental species have carcinogenic nature. In India, significant variations have found in the practices adapted from sampling to analysis for the determination and assessment of the elemental concentration in PM. Therefore, Indian studies (2011-2020) on the related domain are summarized to impart consistency in the field and laboratory practices. Further, a comparative analysis with other countries has also been mentioned in the relevant sections to evaluate its likeness with Indian studies. To prepare this study, literature has been procured from reputed journals. Subsequently, each step from sampling to analysis has thoroughly discussed with quality assurance and control (QA/QC) compliance. In addition, a framework has been proposed that showed field and laboratory analysis in an organized manner. Consequently, this study will provide benefit to novice researcher and improve their understanding about the related subject. Also, it will assist other peoples/bodies in framing the necessary decisions to carry out this study.
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Dao X, Di S, Zhang X, Gao P, Wang L, Yan L, Tang G, He L, Krafft T, Zhang F. Composition and sources of particulate matter in the Beijing-Tianjin-Hebei region and its surrounding areas during the heating season. CHEMOSPHERE 2022; 291:132779. [PMID: 34742769 DOI: 10.1016/j.chemosphere.2021.132779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/25/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
This paper aimed to analyze the composition and pollution sources of particulate matter (PM) in the Beijing-Tianjin-Hebei region and its surrounding areas (henceforth the BTH region) during the heating season to support the mitigation and control of regional air pollution. Manual monitoring data from the China National Environmental Monitoring Network for Atmospheric PM in the BTH region were collected and analyzed during the 2016 and 2018 heating seasons. The positive definite matrix factor analysis (PMF) model was used to analyze the PM sources in BTH cities during the heating season. The main PM components were organic matter (OM), nitrate (NO3-), sulfate (SO42-) and ammonium salt (NH4+). Direct emission sources have decreased since 2016, indicating the effectiveness of governmental controls on these sources; however, secondary pollution showed an increasing trend, suggesting control measures should be strengthened. Daily regional average concentrations of OM, SO42-, NH4+, elemental carbon (EC), chloride (Cl-) and trace elements all showed similar trends. When air quality worsened, the concentrations of the main PM components increased, but trends of change varied among components. In 2018, concentrations of OM and chloride were highest in the Taihang Mountains, and NO3 concentrations were highest in Anyang, Hebi, Jiaozuo and Xinxiang. The SO42- concentration was highest in the southern section of the Taihang Mountains. The NH4+ and EC concentrations were generally highest in the central and southern regions. The concentration of crustal substances was highest in some cities in the north and central parts of the BTH region. In the 2018 heating season, the pollution level of five transmission channels showed an increasing trend in the Northwest, Southeast, Yanshan, South and Taihang Mountain channels. These findings provide a scientific basis for the continued management of atmospheric PM pollution.
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Affiliation(s)
- Xu Dao
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Shiying Di
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Xian Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Panjun Gao
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Li Wang
- Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
| | - Luyu Yan
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Lihuan He
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Thomas Krafft
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Fengying Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China; Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.
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54
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Jangirh R, Ahlawat S, Arya R, Mondal A, Yadav L, Kotnala G, Yadav P, Choudhary N, Rani M, Banoo R, Rai A, Saharan US, Rastogi N, Patel A, Gadi R, Saxena P, Vijayan N, Sharma C, Sharma SK, Mandal TK. Gridded distribution of total suspended particulate matter (TSP) and their chemical characterization over Delhi during winter. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:17892-17918. [PMID: 34686959 DOI: 10.1007/s11356-021-16572-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
In the present study, total suspended particulate matter (TSP) samples were collected at 47 different sites (47 grids of 5 × 5 km2 area) of Delhi during winter (January-February 2019) in campaign mode. To understand the spatial variation of sources, TSP samples were analyzed for chemical compositions including carbonaceous species [organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC)], water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), polycyclic aromatic hydrocarbons (16 PAHs), water-soluble inorganic species (WSIS) (F-, Cl-, SO42-, NO2-, NO3-, PO43-, NH4+, Ca2+, Mg2+, Na+, and K+), and major and minor trace elements (B, Na, Mg, Al, P, S, Cl, K, Ca, Ti, Fe, Zn, Cr, Mn, Cu, As, Pd, F, and Ag). During the campaign, the maximum concentration of several components of TSP (996 μg/m3) was recorded at the Rana Pratap Bagh area, representing a pollution hotspot of Delhi. The maximum concentrations of PAHs were recorded at Udhyog Nagar, a region close to heavily loaded diesel vehicles, small rubber factories, and waste burning areas. Higher content of Cl- and Cl-/Na+ ratio (>1.7) suggests the presence of nonmarine anthropogenic sources of Cl- over Delhi. Minimum concentrations of OC, EC, WSOC, PAHs, and WSIS in TSP were observed at Kalkaji, representing the least polluted area in Delhi. Enrichment factor <5.0 at several locations and a significant correlation of Al with Mg, Fe, Ti, and Ca and C/N ratio indicated the abundance of mineral/crustal dust in TSP over Delhi. Principal component analysis (PCA) was also performed for the source apportionment of TSP, and extracted soil dust was found to be the major contributor to TSP, followed by biomass burning, open waste burning, secondary aerosol, and vehicular emissions.
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Affiliation(s)
- Ritu Jangirh
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Sakshi Ahlawat
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rahul Arya
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Arnab Mondal
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Lokesh Yadav
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
| | - Garima Kotnala
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Pooja Yadav
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Nikki Choudhary
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Martina Rani
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rubiya Banoo
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Akansha Rai
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Ummed Singh Saharan
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Neeraj Rastogi
- Physical Research Laboratory, Navrangpura, Ahmedabad, 380009, India
| | - Anil Patel
- Physical Research Laboratory, Navrangpura, Ahmedabad, 380009, India
| | - Ranu Gadi
- Indira Gandhi Delhi Technical University for Women, New Delhi, 110006, India
| | - Priyanka Saxena
- CSIR - National Environmental Engineering Research Institute, Delhi Zonal Centre, New Delhi, India
| | - Narayanasamy Vijayan
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Chhemendra Sharma
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Sudhir Kumar Sharma
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Tuhin Kumar Mandal
- Environmental Sciences & Biomedical Metrology Division, CSIR - National Physical Laboratory, Dr. K S Krishnan Road, New Delhi, 110012, India.
- Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India.
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55
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Li X, Yan C, Wang C, Ma J, Li W, Liu J, Liu Y. PM 2.5-bound elements in Hebei Province, China: Pollution levels, source apportionment and health risks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150440. [PMID: 34844311 DOI: 10.1016/j.scitotenv.2021.150440] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/27/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
Particle-bound elements have attracted increasing attentions due to their health effects and atmospheric catalytic reactivity. However, elements in atmospheric fine particulate matter (PM2.5) have not been well investigated even in some highly polluted area. In this study, 22 elements in PM2.5 were measured by a multi-metal monitor in ten prefecture-level and county-level cities in Hebei province, one of the most polluted provinces in China, during the heating and non-heating seasons. Source apportionment of PM2.5-bound elements were conducted, and health risks of individual elements and different sources were assessed. The results showed that, total elements (TEs) measured contributed to 2%-7% of the PM2.5 mass, with potassium (K), calcium (Ca), iron (Fe), and zinc (Zn) as the most abundant elements, accounting for about 71%- 87% of TEs mass. Concentrations of chromium (Cr), arsenic (As), and cadmium (Cd) were more likely to exceed the World Health Organization (WHO) limits. Source apportionment results indicated that PM2.5-bound elements were primarily from coal combustion, dust, traffic, ferrous metal smelting and oil combustion, and other industrial related sources. Therein, ferrous metal smelting and oil combustion, coal combustion and industry were the predominant source of Cr, As and Cd, respectively. Health risk assessment indicated that the carcinogenic and non-carcinogenic risks of As for children could exceed the precautionary criteria, and coal combustion source had the highest carcinogenic and non-carcinogenic risks. This study suggested that attentions should be paid not only on PM2.5 mass but also PM2.5-bound compounds especially heavy metals and metalloids to reduce health risks in the future.
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Affiliation(s)
- Xing Li
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Caiqing Yan
- Environment Research Institute, Shandong University, Qingdao 266237, China.
| | - Chunying Wang
- Sailhero Environmental Protection High-tech Co., Ltd, Shijiazhuang 050000, China
| | - Jingjin Ma
- Sailhero Environmental Protection High-tech Co., Ltd, Shijiazhuang 050000, China
| | - Wanxin Li
- Sailhero Environmental Protection High-tech Co., Ltd, Shijiazhuang 050000, China
| | - Junyi Liu
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yue Liu
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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56
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Ravindra K, Singh T, Mandal TK, Sharma SK, Mor S. Seasonal variations in carbonaceous species of PM 2.5 aerosols at an urban location situated in Indo-Gangetic Plain and its relationship with transport pathways, including the potential sources. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 303:114049. [PMID: 34839957 DOI: 10.1016/j.jenvman.2021.114049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 10/19/2021] [Accepted: 10/30/2021] [Indexed: 05/10/2023]
Abstract
The study examines the variation in organic carbon (OC) and elemental carbon (EC) in PM2.5 concentration at an urban location of Indo-Gangetic Plains (IGP) to understand the impact of seasonality and regional crop residue burning activities. Seasonal cluster analysis of backward air masses and concentration-weighted trajectory (CWT) analysis was performed to identify seasonal transport pathways and potential source regions of carbonaceous aerosols. The mean PM2.5 level during the study period was 57 ± 41.6 μgm-3 (5.0-187.3 μgm-3), whereas OC and EC concentration ranges from 2.8 μgm-3 to 28.2 μgm-3 and 1.3 μgm-3 to 15.5 μgm-3 with a mean value of 8.4 ± 5.5 μgm-3 and 5.1 ± 3.3 μgm-3 respectively. The highest mean PM2.5 concentration was found during the winter season (111.3 ± 25.5 μgm-3), which rises 3.6 times compared to the monsoon season. OC and EC also follow a similar trend having the highest levels in winter. Total carbonaceous aerosols contribute ∼38% of PM2.5 composition. The positive linear trend between OC and EC identified the key sources. HYSPLIT cluster analysis of backward air mass trajectories revealed that during the post-monsoon, winters, pre-monsoon, and monsoon, 71%, 81%, 60%, and 43% of air masses originate within the 500 km radius of IGP. CWT analysis and abundance of OC in post-monsoon and winters season establish a linkage between regional solid-biomass fuel use and crop residue burning activities, including meteorology. Moreover, the low annual average OC/EC ratio (1.75) indicates the overall influence of vehicular emissions. The current dataset of carbonaceous aerosols collated with other Indian studies could be used to validate the global aerosol models on a regional scale and aid in evidence-based air pollution reduction strategies.
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Affiliation(s)
- Khaiwal Ravindra
- Department of Environment Studies, Panjab University, Chandigarh, 160014, India.
| | - Tanbir Singh
- Department of Community Medicine, School of Public Health, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India
| | - Tuhin Kumar Mandal
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, New Delhi, 110012, India
| | - Sudhir Kumar Sharma
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, New Delhi, 110012, India
| | - Suman Mor
- Department of Environment Studies, Panjab University, Chandigarh, 160014, India.
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Altuwayjiri A, Pirhadi M, Kalafy M, Alharbi B, Sioutas C. Impact of different sources on the oxidative potential of ambient particulate matter PM 10 in Riyadh, Saudi Arabia: A focus on dust emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150590. [PMID: 34597581 PMCID: PMC8907835 DOI: 10.1016/j.scitotenv.2021.150590] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 05/08/2023]
Abstract
In this study, we employed Principal Component Analysis (PCA) and Multi-Linear Regression (MLR) to identify the most significant sources contributing to the toxicity of PM10 in the city center of Riyadh. PM10 samples were collected using a medium-volume air sampler during cool (December 2019-March 2020) and warm (May 2020-August 2020) seasons, including dust and non-dust events. The collected filters were analyzed for their chemical components (i.e., water-soluble ions, metals, and trace elements) as well as oxidative potential and elemental and organic carbon (EC/OC) contents. Our measurements revealed comparable extrinsic oxidative potential (P-value = 0.30) during the warm (1.2 ± 0.1 nmol/min-m3) and cool (1.1 ± 0.1 nmol/min-m3) periods. Moreover, we observed higher extrinsic oxidative potential of PM10 samples collected during dust events (~30% increase) compared to non-dust samples. Our PCA-MLR analysis identified soil and resuspended dust, secondary aerosol (SA), local industrial activities and petroleum refineries, and traffic emissions as the four sources contributing to the ambient PM10 oxidative potential in central Riyadh. Soil and resuspended dust were the major source contributing to the oxidative potential of ambient PM10, accounting for 31% of the total oxidative potential. Secondary aerosols (SA) were the next important source of PM10 toxicity in the area as they contributed to about 20% of the PM10 oxidative potential. Results of this study revealed the major role of soil and resuspended road dust on PM10 toxicity and can be helpful in adopting targeted air quality policies to reduce the population exposure to PM10.
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Affiliation(s)
- Abdulmalik Altuwayjiri
- University of Southern California, Department of Civil and Environmental Engineering, Los Angeles, CA, USA; Majmaah University, Department of Civil and Environmental Engineering, Majmaah, Riyadh, Saudi Arabia
| | - Milad Pirhadi
- University of Southern California, Department of Civil and Environmental Engineering, Los Angeles, CA, USA
| | - Mohammed Kalafy
- Saudi Envirozone, Air Quality Monitoring Department, Riyadh, Saudi Arabia
| | - Badr Alharbi
- National Center for Environmental Technology, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Constantinos Sioutas
- University of Southern California, Department of Civil and Environmental Engineering, Los Angeles, CA, USA.
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58
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Nayak G, Kumar A, Bikkina S, Tiwari S, Sheteye SS, Sudheer AK. Carbonaceous aerosols and their light absorption properties over the Bay of Bengal during continental outflow. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2022; 24:72-88. [PMID: 34897330 DOI: 10.1039/d1em00347j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The marine atmosphere of the Bay of Bengal (BoB) is prone to get impacted by anthropogenic aerosols from the Indo-Gangetic Plain (IGP) and Southeast Asia (SEA), particularly during the northeast monsoon (NEM). In this study, we quantify and characterize carbonaceous aerosols and their absorption properties collected in two cruise campaigns onboard ORV Sindhu Sadhana during the continental outflow period over the BoB. Aerosol samples were classified based on the air mass back trajectory analyses, wherein samples were impacted by the continental air parcel (CAP), marine air parcel (MAP), and mix of both (CAP + MAP). Significant variability in the PM10 mass concentration (in μg m-3) is found with a maximum value for MAP samples (75.5 ± 36.4) followed by CAP + MAP (58.5 ± 27.3) and CAP (58.5 ± 27.3). The OC/EC ratio (>2) and diagnostic tracers i.e. nss-K+/EC (0.2-0.96) and nss-K+/OC (0.11-1.32) along with the absorption angstrom exponent (AAE: 4.31-6.02) and MODIS (Moderate Resolution Imaging Spectroradiometer) derived fire counts suggest the dominance of biomass burning emission sources. A positive correlation between OC and EC (i.e. r = 0.86, 0.70, and 0.42 for CAP, MAP, and CAP + MAP, respectively) further confirmed the similar emission sources of carbonaceous species. Similarly, a significant correlation between estimated secondary organic carbon (SOC) and water-soluble organic carbon (WSOC; r = 0.99, 0.96, and 0.97 for CAP, MAP, and CAP + MAP, respectively) indicate their similar chemical nature as well as dominant contribution of SOC to WSOC. The absorption coefficient (babs-365) and mass absorption efficiency (MAEBrC-365) of the soluble fraction were estimated at 365 nm wherein, babs-365 showed a linear relationship with WSOC and nss-K+, signifying the contribution of water soluble brown carbon from biomass burning emissions. The estimated MAEBrC-365 (0.30-0.93 m2 g-1), during this study, was consistent with the earlier observations over the BoB, particularly during the continental outflow season.
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Affiliation(s)
- Gourav Nayak
- Geological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa-403 004, India.
| | - Ashwini Kumar
- Geological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa-403 004, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Srinivas Bikkina
- Geological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa-403 004, India.
| | - Shani Tiwari
- Geological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa-403 004, India.
| | - Suhas S Sheteye
- Geological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa-403 004, India.
| | - A K Sudheer
- Physical Research Laboratory, Department of Space, Ahmedabad, India
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59
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Gu Y, Liu B, Dai Q, Zhang Y, Zhou M, Feng Y, Hopke PK. Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China. ENVIRONMENT INTERNATIONAL 2022; 158:106979. [PMID: 34991244 DOI: 10.1016/j.envint.2021.106979] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 11/10/2021] [Indexed: 06/14/2023]
Abstract
Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-Ox normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future.
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Affiliation(s)
- Yao Gu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Baoshuang Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China.
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Ming Zhou
- State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Philip K Hopke
- Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY 13699, USA
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Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci Rep 2021; 11:24232. [PMID: 34930975 PMCID: PMC8688545 DOI: 10.1038/s41598-021-03650-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/07/2021] [Indexed: 11/08/2022] Open
Abstract
The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of [Formula: see text] based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate [Formula: see text] concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.
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Wang M, Tian P, Wang L, Yu Z, Du T, Chen Q, Guan X, Guo Y, Zhang M, Tang C, Chang Y, Shi J, Liang J, Cao X, Zhang L. High contribution of vehicle emissions to fine particulate pollutions in Lanzhou, Northwest China based on high-resolution online data source appointment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 798:149310. [PMID: 34340091 DOI: 10.1016/j.scitotenv.2021.149310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/20/2021] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
The quantitative estimation of urban particulate matter (PM) sources is essential but limited because of various reasons. The hourly online data of PM2.5, organic carbon (OC), elemental carbon (EC), water-soluble ions, and elements from December 2019 to November 2020 was used to conduct PM source appointment, with an emphasis on the contribution of vehicle emissions to fine PM pollution in downtown Lanzhou, Northwest China. Vehicle emissions, secondary formation, mineral dust, and coal combustion were found to be the major PM sources using the positive matrix factorization model. The seasonal mean PM2.5 were estimated to be 72.8, 39.2, 24.3, and 43.6 μg·m-3 and vehicle emissions accounted for 35.7%, 25.8%, 30.0%, and 56.6% in winter, spring, summer, and autumn, respectively. Vehicle emissions were the largest source of PM considering the high PM pollution in winter and its significantly large contribution in autumn. Furthermore, the contribution of vehicle emissions increased with increasing PM in winter and autumn. Vehicle emissions were also the most important source of EC, accounting for 70.3%, 91.0%, 83.5%, and 93.7% of the total EC in winter, spring, summer, and autumn, respectively. With the reduction in industrial emissions and increase in vehicle numbers in China in recent years, vehicle emissions are going to be the largest source of urban PM pollution. To sustainably improve air quality in Lanzhou and other Chinese cities, efforts should be made to control vehicle emissions such as promoting clean-energy vehicles and encouraging public transportation.
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Affiliation(s)
- Min Wang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Pengfei Tian
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Ligong Wang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Zeren Yu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Tao Du
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Qiang Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Xu Guan
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yumin Guo
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Min Zhang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Chenguang Tang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yi Chang
- Gansu Province Environmental Monitoring Center, Lanzhou 730020, China
| | - Jinsen Shi
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
| | - Jiening Liang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Xianjie Cao
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Lei Zhang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
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Zhou M, Li T, Liu P, Zhang S, Liu Y, An T, Zhao H. Real-time on-site monitoring of soil ammonia emissions using membrane permeation-based sensing probe. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 289:117850. [PMID: 34358875 DOI: 10.1016/j.envpol.2021.117850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/05/2021] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
An ability to real-time, continuously monitor soil ammonia emission profiles under diverse meteorological conditions with high temporal resolution in a simple and maintenance-free fashion can provide the urgently needed scientific insights to mitigate ammonia emission to the atmosphere and improve agricultural fertilization practice. Here, we report an open-chamber deployment unit embedded a gas-permeable membrane-based conductometric sensing probe (OC-GPMCP) capable of on-site continuously monitoring soil ammonia emission flux ( [Formula: see text] ) -time (t) profiles without the need for ongoing calibration. The developed OC-GPMCPs were deployed to a sugarcane field and a cattle farm under different fertilization/meteorological conditions to exemplify their real-world applicability for monitoring soil ammonia emission from agricultural land and livestock farm, respectively. The obtained [Formula: see text] - t profiles from the sugarcane field unveil that the ammonia emission rate is largely determined by fertilization methods and meteorological conditions. While the [Formula: see text] - t profiles from the cattle farm can be decisively correlated to various meteorological conditions. The reported OC-GPMCP is cheap to fabricate, easy to deploy, and maintenance-free to operate. These advantageous features make OC-GPMCP an effective analytical tool for large-scale soil ammonia emission assessment under diverse meteorological conditions, providing critically important scientific insights to mitigate ammonia emission into the atmosphere and improve agricultural fertilization practice.
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Affiliation(s)
- Ming Zhou
- Centre for Clean Environment and Energy, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Tianling Li
- Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China
| | - Porun Liu
- Centre for Clean Environment and Energy, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Shanqing Zhang
- Centre for Clean Environment and Energy, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Yang Liu
- College of Science and Engineering, James Cook University, Townsville, QLD, 4811, Australia
| | - Taicheng An
- Guangzhou Key Laboratory Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China
| | - Huijun Zhao
- Centre for Clean Environment and Energy, Griffith University, Gold Coast, QLD, 4222, Australia.
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Gupta L, Dev R, Zaidi K, Sunder Raman R, Habib G, Ghosh B. Assessment of PM 10 and PM 2.5 over Ghaziabad, an industrial city in the Indo-Gangetic Plain: spatio-temporal variability and associated health effects. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:735. [PMID: 34669030 DOI: 10.1007/s10661-021-09411-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
This study examined the PM10 and PM2.5 concentration, associated mortality, and transport pathways in Ghaziabad which is an industrial city in the Indo-Gangetic Plain. To achieve this, PM (both PM10 and PM2.5) and meteorological parameters were measured from June 2018 to May 2019 at 2 locations and analyzed together with data from a 3rd location in Ghaziabad. The highest daily average PM10 and PM2.5 concentrations were ~ 1000 µg m-3 and ~ 450 µg m-3, respectively. At each of the three locations, the annual mean PM10 concentrations were ~ 260 ± 150 µg m-3 while the PM2.5 concentrations were 140 ± 90 µg m-3. Nonparametric Spearman rank correlation analysis between meteorological parameters and PM concentrations indicated that ventilation coefficient was anti-correlated with PM concentration during the post-monsoon and winter seasons (the most polluted seasons) with rank correlation values of approximately - 0.50. Multiple linear regression (MLR) revealed that the variability in local meteorological parameters account for ~ 50% variability (maximum) in PM10 mass during the monsoon and PM2.5 during the post-monsoon season. For long-range sources, cluster and concentrated weighted trajectory (CWT) analyses utilizing regional meteorology showed the impact of transported PM from sources in Arabian sea through western India in monsoon and from parts of South Asia through Northwestern IGP and neighboring cities in Uttar Pradesh in other seasons. Finally, mortality estimates show that the number of deaths attributable to ambient PM2.5 in Ghaziabad were ~ 873 per million individuals which was ~ 70% higher than Delhi.
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Affiliation(s)
- Lovleen Gupta
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
- Department of Environmental Engineering, Delhi Technological University, Delhi, 110042, India
| | - Rishabh Dev
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
| | - Kumail Zaidi
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
| | - Ramya Sunder Raman
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
- Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Bhopal Bypass Road, Bhauri, Bhopal, Madhya Pradesh, 462066, India
| | - Gazala Habib
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India.
| | - Bipasha Ghosh
- Department of Civil Engineering, Indian Institute of Technology, Delhi, 110016, India
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Alias A, Latif MT, Othman M, Azhari A, Abd Wahid NB, Aiyub K, Khan MF. Compositions, source apportionment and health risks assessment of fine particulate matter in naturally-ventilated schools. ATMOSPHERIC POLLUTION RESEARCH 2021; 12:101190. [DOI: 10.1016/j.apr.2021.101190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Lei X, Chen R, Li W, Cheng Z, Wang H, Chillrud S, Yan B, Ying Z, Cai J, Kan H. Personal exposure to fine particulate matter and blood pressure: Variations by particulate sources. CHEMOSPHERE 2021; 280:130602. [PMID: 34162067 DOI: 10.1016/j.chemosphere.2021.130602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/27/2021] [Accepted: 04/13/2021] [Indexed: 06/13/2023]
Abstract
Fine particulate matter (PM2.5) is a complex mixture of components which has been associated with various cardiovascular effects, such as elevated blood pressure (BP). However, evidences on specific sources behind these effects remain uncertain. Based on 140 72-h personal measurements among a panel of 36 health college students in Shanghai, China, we assessed associations between source-apportioned PM2.5 exposure and BP changes. Based on personal filter samples, PM2.5 source apportionment was conducted using Positive Matrix Factorization (PMF) model. Linear mixed-effects models were applied to evaluate associations of source-specific PM2.5 exposure with BP changes. Seven sources were identified in PMF analysis. Among them, secondary sulfate (41%) and nitrate (24%) sources contributed most to personal PM2.5, followed by industrial emissions (15%), traffic-related source (10%), coal combustion (6.2%), dust (2.4%) and aged sea salt (1.1%). We found nitrate, traffic-related source and coal combustion were significantly associated with increased BP. For example, an interquartile range increase in PM2.5 from traffic-related source was significantly associated with increase in systolic BP [1.5 (95% CI: 0.26, 2.7) mmHg], diastolic BP [1.2 (95% CI: 0.10, 2.2) mmHg] and mean arterial pressure [1.2 (95% CI: 0.15, 2.2) mmHg]. This is the first investigation linking personal PM2.5 source profile and BP changes. This study provides evidence that several anthropogenic emissions (especially traffic-related emission) may be particularly responsible for BP increases, and highlights that the importance of development of health-oriented PM2.5 source control strategies.
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Affiliation(s)
- Xiaoning Lei
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Department of Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Weihua Li
- Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Zhen Cheng
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, China
| | - Steven Chillrud
- Division of Geochemistry, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
| | - Beizhan Yan
- Division of Geochemistry, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
| | - Zhekang Ying
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, 200030, China.
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Research, Institute of Reproduction and Development, Fudan University, Shanghai, China.
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Sharma SK, Mukherjee S, Choudhary N, Rai A, Ghosh A, Chatterjee A, Vijayan N, Mandal TK. Seasonal variation and sources of carbonaceous species and elements in PM 2.5 and PM 10 over the eastern Himalaya. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:51642-51656. [PMID: 33990919 DOI: 10.1007/s11356-021-14361-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/07/2021] [Indexed: 05/10/2023]
Abstract
The study represents the seasonal characteristics (carbonaceous aerosols and elements) and the contribution of prominent sources of PM2.5 and PM10 in the high altitude of the eastern Himalaya (Darjeeling) during August 2018-July 2019. Carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), and water soluble organic carbon (WSOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Cr, Ni, Mo, Cl, P, S, K, Zr, Pb, Na, Mg, Ca, and B) in PM2.5 and PM10 were analyzed to estimate their possible sources. The annual concentrations of PM2.5 and PM10 were computed as 37±12 μg m-3 and 58±18 μg m-3, respectively. In the present case, total carbonaceous species in PM2.5 and PM10 were accounted for 20.6% of PM2.5 and 18.6% of PM10, respectively, whereas trace elements in PM2.5 and PM10 were estimated to be 15% of PM2.5 and 12% of PM10, respectively. Monthly and seasonal variations in mass concentrations of carbonaceous aerosols and elements in PM2.5 and PM10 were also observed during the observational period. In PM2.5, the annual concentrations of POC and SOC were 2.35 ± 1.06 μg m-3 (66% of OC) and 1.19±0.57 μg m-3 (34% of OC), respectively, whereas annual average POC and SOC concentrations in PM10 were 3.18 ± 1.13 μg m-3 (63% of OC) and 2.05±0.98 μg m-3 (37% of OC), respectively. The seasonal contribution of POC and SOC were ranging from 55 to 77% and 33 to 45% of OC in PM2.5, respectively, whereas in PM10, the seasonal contributions of POC and SOC were ranging from 51 to 73% and 37 to 49% of OC, respectively. The positive relationship between OC & EC and OC & WSOC of PM2.5 and PM10 during all the seasons (except monsoon in case of PM10) indicates their common sources. The enrichment factors (EFs) and significant positive correlation of Al with othe crustal elements (Fe, Ca, Mg, and Ti) of fine and coarse mode aerosols indicate the influence of mineral dust at Darjeeling. Principal component analysis (PCA) resolved the four common sources (biomass burning + fossil fuel combustion (BB + FFC), crustal/soil dust, vehicular emissions (VE), and industrial emissions (IE)) of PM2.5 and PM10 in Darjeeling.
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Affiliation(s)
- Sudhir Kumar Sharma
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India.
| | - Sauryadeep Mukherjee
- Centre for Astroparticle Physics and Space Sciences, Bose Institute, Darjeeling, 734 102, India
| | - Nikki Choudhary
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - Akansha Rai
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
| | - Abhinandan Ghosh
- Centre for Astroparticle Physics and Space Sciences, Bose Institute, Darjeeling, 734 102, India
| | - Abhijit Chatterjee
- Centre for Astroparticle Physics and Space Sciences, Bose Institute, Darjeeling, 734 102, India
| | - Narayanswami Vijayan
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
| | - Tuhin Kumar Mandal
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India
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Chemical Composition and Source Apportionment of Total Suspended Particulate in the Central Himalayan Region. ATMOSPHERE 2021. [DOI: 10.3390/atmos12091228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region.
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Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167326] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.
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Nozza E, Valentini S, Melzi G, Vecchi R, Corsini E. Advances on the immunotoxicity of outdoor particulate matter: A focus on physical and chemical properties and respiratory defence mechanisms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 780:146391. [PMID: 33774291 DOI: 10.1016/j.scitotenv.2021.146391] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/16/2021] [Accepted: 03/07/2021] [Indexed: 06/12/2023]
Abstract
Particulate matter (PM) is acknowledged to have multiple detrimental effects on human health. In this review, we report literature results on the possible link between outdoor PM and health outcomes with a focus on pulmonary infections and the mechanisms responsible for observed negative effects. PM physical and chemical properties, such as size and chemical composition, as well as major emission sources are described for a more comprehensive view about the role played by atmospheric PM in the observed adverse health effects; to this aim, major processes leading to the deposition of PM in the respiratory tract and how this can pave the way to the onset of pathologies are also presented. From the literature works here reviewed, two ways in which PM can threaten human health promoting respiratory infectious diseases are mostly taken into account. The first pathway is related to an enhanced susceptibility and here we will also report on molecular mechanisms in the lung immune system responsible for the augmented susceptibility to pathogens, such as the damage of mechanical defensive barriers, the alteration of the innate immune response, and the generation of oxidative stress. The second one deals with the relationship between infectious agents and PM; here we recall that viruses and bacteria (BioPM) are themselves part of atmospheric PM and are collected during sampling together with particles of different origin; so, data should be analysed with caution in order to avoid any false cause-effect relation. To face these issues a multidisciplinary approach is mandatory as also evident from the ongoing research about the mechanisms hypothesized for the SARS-CoV-2 airborne spreading, which is still controversial and claims for further investigation. Therefore, we preferred not to include papers dealing with SARS-CoV-2.
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Affiliation(s)
- E Nozza
- Department of Environmental Science and Policy, Università degli Studi di Milano, via Balzaretti 9, 20133 Milan, Italy; Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133 Milan, Italy
| | - S Valentini
- Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133 Milan, Italy
| | - G Melzi
- Department of Pharmacological and Biomolecular Sciences (DiSFeB), Università degli Studi di Milano, via Balzaretti 9, 20133 Milan, Italy
| | - R Vecchi
- Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133 Milan, Italy.
| | - E Corsini
- Department of Environmental Science and Policy, Università degli Studi di Milano, via Balzaretti 9, 20133 Milan, Italy
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70
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Singh G, Prakash J, Ray SK, Yawar M, Habib G. Development and evaluation of air pollution-linked quality of life (AP-QOL) questionnaire: insight from two different cohorts. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:43459-43475. [PMID: 33835344 DOI: 10.1007/s11356-021-13754-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
In this study, the air pollution-related quality of life (AP-QOL) questionnaire was carried out in two geographically and economically different groups including New Delhi (Megacity) and Hamirpur, Himachal Pradesh (town), and APE scores were linked with respiratory and cardiovascular illness. The APE-Score was developed by AP-QOL questionnaire responses using Delphi technique and further analyzed using principal component analysis (PCA). For reliability of APE-Score and AP-QOL questionnaire, α-Cronbach's test and basic statistics were performed. The linear mixed-effect model and odds ratios were used to evaluate air pollution exposure and health outcomes. Overall, 720 academicians and 276 security guards were invited to participate in the questionnaire. Cronbach's α coefficients ranged from 0.70 to 0.84 indicated significant reliability in the AP-QOL questionnaire conducted in this study. Substantial variation in respiratory symptoms and their medical history were found - 76.9% ([95% confidential interval (CI)]: (- 83.8, - 66.9) (p < 0.05)) and - 28.6% (95% CI: (- 37.8, - 18.0) (p < 0.05)), respectively, with interquartile range (IQR) increase of APE score. The odds ratios (ORs) of respiratory medical history (MH Res.) showed a significant increase from 1.01 to 1.35 for low to high air pollution exposure in the academic group of IIT Delhi. Interestingly, for an academic group of NITH, the ORs for medical history of cardiovascular (MH Card.) showed an increase from 1.08 to 1.13 for low to high APE which was not the case for IIT Delhi academicians.
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Affiliation(s)
- Gaurav Singh
- Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
- Department of Local Self-Government, Barmer, Rajasthan, India
| | - Jai Prakash
- Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
- Aerosol and Air Quality Research Laboratory, Washington University in St. Louis, St. Louis, MO, USA
| | - Sanjeev Kumar Ray
- Department of Civil Engineering, National Institute of Technology, Hamirpur, India
| | - Mohammad Yawar
- Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Gazala Habib
- Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
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71
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Althuwaynee OF, Pokharel B, Aydda A, Balogun AL, Kim SW, Park HJ. Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2021; 31:709-726. [PMID: 33159165 DOI: 10.1038/s41370-020-00271-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
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Affiliation(s)
- Omar F Althuwaynee
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-ro Gwangjin-gu, Seoul, 05006, Republic of Korea
| | - Badal Pokharel
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Ali Aydda
- Department of Geology, Faculty of Sciences, Ibn Zohr University, B.P 8106, 80000, Agadir, Morocco
| | - Abdul-Lateef Balogun
- Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, 32610, Perak, Malaysia.
| | - Sang-Wan Kim
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-ro Gwangjin-gu, Seoul, 05006, Republic of Korea
| | - Hyuck-Jin Park
- Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-ro Gwangjin-gu, Seoul, 05006, Republic of Korea
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72
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Hama S, Kumar P, Alam MS, Rooney DJ, Bloss WJ, Shi Z, Harrison RM, Crilley LR, Khare M, Gupta SK. Chemical source profiles of fine particles for five different sources in Delhi. CHEMOSPHERE 2021; 274:129913. [PMID: 33979925 DOI: 10.1016/j.chemosphere.2021.129913] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/31/2021] [Accepted: 02/05/2021] [Indexed: 06/12/2023]
Abstract
Increasing emissions from sources such as construction and burning of biomass from crop residues, roadside and municipal solid waste have led to a rapid increase in the atmospheric concentrations of fine particulate matter (≤2.5 μm; PM2.5) over many Indian cities. Analyses of their chemical profiles are important for receptor models to accurately estimate the contributions from different sources. We have developed chemical source profiles for five important pollutant sources - construction (CON), paved road dust (PRD), roadside biomass burning (RBB), solid waste burning (SWB), and crop residue burning (CPB) - during three intensive campaigns (winter, summer and post-monsoon) in and around Delhi. We obtained chemical characterisations of source profiles incorporating carbonaceous material such as organic carbon (OC) and elemental carbon (EC), water-soluble ions (F-, Cl-, NO2-, NO3-, SO42-, PO43-, Na+ and NH4+), and elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Br, Rb, Sr, Ba, and Pb). CON was dominated by the most abundant elements, K, Si, Fe, Al, and Ca. PRD was also dominated by crustal elements, accounting for 91% of the total analysed elements. RBB, SWB and CPB profiles were dominated by organic matter, which accounted for 94%, 86.2% and 86% of the total PM2.5, respectively. The database of PM emission profiles developed from the sources investigated can be used to assist source apportionment studies for accurate quantification of the causes of air pollution and hence assist governmental bodies in formulating relevant countermeasures.
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Affiliation(s)
- Sarkawt Hama
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland.
| | - Mohammed S Alam
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Daniel J Rooney
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - William J Bloss
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Zongbo Shi
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Roy M Harrison
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; Also at: Dept of Environmental Sciences/Center of Excellence in Environmental Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Leigh R Crilley
- Department of Chemistry, York University, Toronto, ON, Canada
| | - Mukesh Khare
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Sanjay Kumar Gupta
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
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73
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Source Apportionment and Health Risk Assessment of Metal Elements in PM2.5 in Central Liaoning’s Urban Agglomeration. ATMOSPHERE 2021. [DOI: 10.3390/atmos12060667] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To better understand the source and health risk of metal elements in PM2.5, a field study was conducted from May to December 2018 in the central region of the Liaoning province, China, including the cities of Shenyang, Anshan, Fushun, Benxi, Yingkou, Liaoyang, and Tieling. 24 metal elements (Na, K, V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sn, Sb, Pb, Bi, Al, Sr, Mg, Ti, Ca, Fe, Ba, and Si) in PM2.5 were measured by ICP-MS and ICP-OES. They presented obvious seasonal variations, with the highest levels in winter and lowest in summer for all seven cities. The sum of 24 elements were ranged from to in these cities. The element mass concentration ratio was the highest in Yingkou in the spring (26.15%), and the lowest in Tieling in winter (3.63%). The highest values of elements in PM2.5 were mostly found in Anshan and Fushun among the studied cities. Positive matrix factorization (PMF) modelling revealed that coal combustion, industry, traffic emission, soil dust, biomass burning, and road dust were the main sources of measured elements in all cities except for Yingkou. In Yingkou, the primary sources were identified as coal combustion, metal smelting, traffic emission, soil dust, and sea salt. Health risk assessment suggested that Mn had non-carcinogenic risks for both adults and children. As for Cr, As, and Cd, there was carcinogenic risks for adults and children in most cities. This study provides a clearer understanding of the regional pollution status of industrial urban agglomeration.
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74
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Goel V, Hazarika N, Kumar M, Singh V, Thamban NM, Tripathi SN. Variations in Black Carbon concentration and sources during COVID-19 lockdown in Delhi. CHEMOSPHERE 2021; 270:129435. [PMID: 33412356 PMCID: PMC8021479 DOI: 10.1016/j.chemosphere.2020.129435] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 05/08/2023]
Abstract
A nationwide lockdown was imposed in India due to COVID-19 pandemic in five phases from 25th March to May 31, 2020. The lockdown restricted major anthropogenic activities, primarily vehicular and industrial, thereby reducing the particulate matter concentration. This work investigates the variation in Black Carbon (BC) concentration and its sources (primarily Fossil Fuel (ff) burning and Biomass Burning (bb)) over Delhi from 18th February to July 31, 2020, covering one month of pre-lockdown phase, all the lockdown phases, and two months of successive lockdown relaxations. The daily average BC concentration varied from 0.22 to 16.92 μg/m3, with a mean value of 3.62 ± 2.93 μg/m3. During Pre-Lockdown (PL, 18th Feb-24th March 2020), Lockdown-1 (L1, 25th March-14th April 2020), Lockdown-2 (L2, 15th April-3rd May 2020), Lockdown-3 (L3, 4th-17th May 2020), Lockdown-4 (L4, 18th-31st May 2020), Unlock-1 (UN1, June 2020), and Unlock-2 (UN2, July 2020) the average BC concentrations were 7.93, 1.73, 2.59, 3.76, 3.26, 2.07, and 2.70 μg/m3, respectively. During the lockdown and unlock phases, BC decreased up to 78% compared to the PL period. The BC source apportionment studies show that fossil fuel burning was the dominant BC source during the entire sampling period. From L1 to UN2 an increasing trend in BCff contribution was observed (except L3) due to the successive relaxations given to anthropogenic activities. BCff contribution dipped briefly during L3 due to the intensive crop residue burning events in neighboring states. CWT analysis showed that local emission sources were the dominant contributors to BC concentration over Delhi.
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Affiliation(s)
- Vikas Goel
- Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Naba Hazarika
- Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Mayank Kumar
- Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
| | - Vikram Singh
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
| | - Navaneeth M Thamban
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh, 208016, India
| | - Sachchida Nand Tripathi
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Uttar Pradesh, 208016, India.
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75
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Miech JA, Herckes P, Fraser MP. Effect of COVID-19 travel restrictions on Phoenix air quality after accounting for boundary layer variations. ATMOSPHERIC ENVIRONMENT: X 2021; 10:100105. [PMID: 33778482 PMCID: PMC7981586 DOI: 10.1016/j.aeaoa.2021.100105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 01/27/2021] [Indexed: 06/12/2023]
Abstract
Due to the global response to the COVID-19 pandemic, there have been a variety of policy responses that have produced a range of expected and unexpected effects on society and our surrounding environment. One widely reported result of the pandemic response is that travel restrictions have resulted in improvements in regional air quality. This study aims to determine the effect of COVID-19 related Stay at Home precautions on air quality in a metropolitan area. We specifically focus on CO, NO2, and PM10 in Maricopa County (Phoenix), Arizona, as these all contribute to local air quality concerns. The role of meteorological parameters on ambient concentrations for these pollutants was investigated by using the local planetary boundary layer height (PBH) to account for vertical mixing. Across all three sites studied, there was no uniform decrease in either CO or NO2, even when freeway traffic volume was down by ~35%. For PM10, there was a significant decrease of ~45% seen at all the sites for the period most directly impacted by local Stay at Home restrictions compared to the past two years. This indicates that different pollutants have fundamentally different behavior in the local environment and suggests that these pollutants originate from different sources.
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Affiliation(s)
- Jason A Miech
- School of Molecular Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | - Pierre Herckes
- School of Molecular Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | - Matthew P Fraser
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, 85287, USA
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76
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Dumka UC, Kaskaoutis DG, Mihalopoulos N, Sheoran R. Identification of key aerosol types and mixing states in the central Indian Himalayas during the GVAX campaign: the role of particle size in aerosol classification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 761:143188. [PMID: 33143923 DOI: 10.1016/j.scitotenv.2020.143188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/15/2020] [Accepted: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM1 and PM10). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. absorption Ångström exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named "large/BC mix" dominates in both PM1 (45.4%) and PM10 (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE < 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM1, while in PM10, a large fraction (39%) corresponds to "large/low-absorbing" aerosols and only 3.9% is characterized as "BC-dominated". The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole.
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Affiliation(s)
- U C Dumka
- Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital 263 001, India.
| | - D G Kaskaoutis
- Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Palaia Penteli, 15236 Athens, Greece; Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 71003 Crete, Greece.
| | - N Mihalopoulos
- Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Palaia Penteli, 15236 Athens, Greece; Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 71003 Crete, Greece
| | - Rahul Sheoran
- Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital 263 001, India
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77
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Kumar P, Kalaiarasan G, Porter AE, Pinna A, Kłosowski MM, Demokritou P, Chung KF, Pain C, Arvind DK, Arcucci R, Adcock IM, Dilliway C. An overview of methods of fine and ultrafine particle collection for physicochemical characterisation and toxicity assessments. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 756:143553. [PMID: 33239200 DOI: 10.1016/j.scitotenv.2020.143553] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/08/2020] [Accepted: 11/02/2020] [Indexed: 06/11/2023]
Abstract
Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular diseases. The smaller size fractions, ≤2.5 μm (PM2.5; fine particles) and ≤0.1 μm (PM0.1; ultrafine particles), show the highest bioactivity but acquiring sufficient mass for in vitro and in vivo toxicological studies is challenging. We review the suitability of available instrumentation to collect the PM mass required for these assessments. Five different microenvironments representing the diverse exposure conditions in urban environments are considered in order to establish the typical PM concentrations present. The highest concentrations of PM2.5 and PM0.1 were found near traffic (i.e. roadsides and traffic intersections), followed by indoor environments, parks and behind roadside vegetation. We identify key factors to consider when selecting sampling instrumentation. These include PM concentration on-site (low concentrations increase sampling time), nature of sampling sites (e.g. indoors; noise and space will be an issue), equipment handling and power supply. Physicochemical characterisation requires micro- to milli-gram quantities of PM and it may increase according to the processing methods (e.g. digestion or sonication). Toxicological assessments of PM involve numerous mechanisms (e.g. inflammatory processes and oxidative stress) requiring significant amounts of PM to obtain accurate results. Optimising air sampling techniques are therefore important for the appropriate collection medium/filter which have innate physical properties and the potential to interact with samples. An evaluation of methods and instrumentation used for airborne virus collection concludes that samplers operating cyclone sampling techniques (using centrifugal forces) are effective in collecting airborne viruses. We highlight that predictive modelling can help to identify pollution hotspots in an urban environment for the efficient collection of PM mass. This review provides guidance to prepare and plan efficient sampling campaigns to collect sufficient PM mass for various purposes in a reasonable timeframe.
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Affiliation(s)
- Prashant Kumar
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland.
| | - Gopinath Kalaiarasan
- Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - Alexandra E Porter
- Department of Materials, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom
| | - Alessandra Pinna
- Department of Materials, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom
| | - Michał M Kłosowski
- Department of Materials, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom
| | - Philip Demokritou
- Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 665 Huntington Avenue, Room 1310, Boston, MA 02115, USA
| | - Kian Fan Chung
- National Heart & Lung Institute, Imperial College London, London SW3 6LY, United Kingdom
| | - Christopher Pain
- Department of Earth Science & Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - D K Arvind
- Centre for Speckled Computing, School of Informatics, University of Edinburgh, Edinburgh, Scotland EH8 9AB, United Kingdom
| | - Rossella Arcucci
- Data Science Institute, Department of Computing, Imperial College London, London SW7 2BU, United Kingdom
| | - Ian M Adcock
- National Heart & Lung Institute, Imperial College London, London SW3 6LY, United Kingdom
| | - Claire Dilliway
- Department of Earth Science & Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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78
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Spatio-Temporal Characteristics of PM2.5, PM10, and AOD over the Central Line Project of China’s South-North Water Diversion in Henan Province (China). ATMOSPHERE 2021. [DOI: 10.3390/atmos12020225] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The spatio-temporal characteristics of particulate matter with a particle size less than or equal to 2.5 μm (PM2.5), particulate matter with a particle size less than or equal to 10 μm (PM10), meteorological parameters from September 2018 to September 2019, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol optical depth (AOD) from 2007 to 2019 were investigated over the Central Line Project of China’s South-North Water Diversion (CSNWD) in Henan Province. To better understand the characteristics of the atmospheric environment over the CSNWD, air quality monitoring stations were installed in Nanyang (in the upper reaches), Zhengzhou (in the middle reaches), and Anyang (in the lower reaches). In this study, daily, monthly, and seasonal statistical analyses of PM2.5 and PM10 concentrations were performed and their relationship with meteorological parameters was investigated. The results show extremely poor air quality conditions over the Zhengzhou Station compared with the Nanyang and Anyang Stations. The annual average PM2.5 concentration did not meet China’s ambient air secondary standard (35 μg/m3 annual mean) over all the stations, while the annual average PM10 concentration satisfied China’s ambient air secondary standard (100 μg/m3 annual mean) over the Anyang and Nanyang Stations, except for the Zhengzhou Station. The highest PM2.5 and PM10 concentrations were observed during winter compared with the other seasons. The results show that PM2.5 and PM10 concentrations were negatively correlated with wind speed and temperature at the Nanyang and Zhengzhou Stations, but positively correlated with relative humidity. However, no significant negative or positive correlation was observed at Anyang Station. There is a strong linear positive correlation between PM2.5 and PM10 (R = 0.99), which indicates that the particulate matter at the three stations was mainly caused by local emissions. Additionally, the AOD values at the three stations were the highest in summer, which may be related to the residues of crops burned in Henan Province in summer.
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79
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Dumka UC, Kaskaoutis DG, Verma S, Ningombam SS, Kumar S, Ghosh S. Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations. ATMOSPHERIC POLLUTION RESEARCH 2021; 12:225-242. [PMID: 36915905 PMCID: PMC9996264 DOI: 10.1016/j.apr.2020.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 05/16/2023]
Abstract
The current study examines the impact of the COVID-19 lockdown (25th March until May 17, 2020) period in particulate matter (PM) concentrations and air pollutants (NOx, SO2, CO, NH3, and O3) at 63 stations located at Delhi, Uttar Pradesh and Haryana states within the Delhi-NCR, India. Large average reductions are recorded between the stations in each state such as PM10 (-46 to -58%), PM2.5 (-49 to -55%), NO2 (-27 to -58%), NO (-54% to -59%), CO (-4 to -44%), NH3 (-2 to -38%), while a slight increase is observed for O3 (+4 to +6%) during the lockdown period compared to same periods in previous years. Furthermore, PM and air pollutants are significantly reduced during lockdown compared to the respective period in previous years, while a significant increase in pollution levels is observed after the re-opening of economy. The meteorological changes were rather marginal between the examined periods in order to justify such large reductions in pollution levels, which are mostly attributed to traffic-related pollutants (NOx, CO and road-dust PM). The WRF-CHIMERE model simulations reveal a remarkable reduction in PM2.5, NO2 and SO2 levels over whole Indian subcontinent and mostly over urban areas, due to limitation in emissions from the traffic and industrial sectors. A PM2.5 reduction of -48% was simulated in Delhi in great consistency with measurements, rendering the model as a powerful tool for simulations of lower pollution levels during lockdown period.
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Affiliation(s)
- U C Dumka
- Aryabhatta Research Institute of Observational Sciences, Nainital, 263001, India
| | - D G Kaskaoutis
- Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Palaia Penteli, 15236, Athens, Greece
- Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 71003, Crete, Greece
| | - Shubha Verma
- Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
| | | | - Sarvan Kumar
- Department of Earth and Planetary Sciences, Prof. Rajendra Singh (Rajju Bhaiya) Institute of Physical Sciences for Study and Research, Veer Bahadur Singh Purvanchal University, Jaunpur, 222003, Uttar Pradesh, India
| | - Sanhita Ghosh
- Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
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80
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Zhao S, Tian H, Luo L, Liu H, Wu B, Liu S, Bai X, Liu W, Liu X, Wu Y, Lin S, Guo Z, Lv Y, Xue Y. Temporal variation characteristics and source apportionment of metal elements in PM 2.5 in urban Beijing during 2018-2019. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115856. [PMID: 33120143 DOI: 10.1016/j.envpol.2020.115856] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/05/2020] [Accepted: 10/12/2020] [Indexed: 06/11/2023]
Abstract
To explore high-resolution temporal variation characteristics of atmospheric metal elements concentration and more accurate pollution sources apportionment, online monitoring of metal elements in PM2.5 with 1-h time resolution was conducted in Beijing from August 22, 2018 to August 21, 2019. Concentration of 18 elements varied between detection limit (ranging from 0.1 to 100 ng/m3) and nearly 25 μg/m3. Si, Fe, Ca, K and Al represented major elements and accounted for 93.47% of total concentration during the study period. Compared with previous studies, airborne metal pollution in Beijing has improved significantly which thanks to strict comprehensive control measures under the Clean Air Action Plan since 2013. Almost all elements present higher concentrations on weekdays than weekends, while concentrations of elements associated with dust sources during holidays are higher than those in working days after the morning peak, and there is almost no concentration difference in the evening peak period. Soil and dust, vehicle non-exhaust emissions, biomass, industrial processes and fuel combustion were apportioned as main sources of atmospheric metal pollution, accounting for 63.6%, 18.4%, 16.8%, 1.0% and 0.18%, respectively. Furthermore, main occurrence season of metal pollution is judged by characteristic radar chart of varied metal elements proposed for the first time in this study, for example, fuel combustion type pollution mainly occurs in winter and spring. Results of 72-h backward trajectory analysis of air masses showed that, except for local emissions, atmospheric metal pollution in Beijing is also affected by regional transport from Inner Mongolia, Hebei, the Bohai Sea and Heilongjiang.
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Affiliation(s)
- Shuang Zhao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China.
| | - Lining Luo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Huanjia Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Bobo Wu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shuhan Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Xiaoxuan Bai
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Wei Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Xiangyang Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yiming Wu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shumin Lin
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Zhihui Guo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yunqian Lv
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yifeng Xue
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China; National Engineering Research Center of Urban Environmental Pollution Control, Beijing Municipal Research Institute of Environmental Protection, Beijing, 100037, China
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81
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Jain S, Sharma SK, Vijayan N, Mandal TK. Investigating the seasonal variability in source contribution to PM 2.5 and PM 10 using different receptor models during 2013-2016 in Delhi, India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:4660-4675. [PMID: 32946053 DOI: 10.1007/s11356-020-10645-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/26/2020] [Indexed: 05/26/2023]
Abstract
The present work deals with the seasonal variations in the contribution of sources to PM2.5 and PM10 in Delhi, India. Samples of PM2.5 and PM10 were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM2.5 and PM10 were 131 ± 79 μg m-3 and 238 ± 106 μg m-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM2.5 and PM10 were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM2.5 and PM10 as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM2.5 and PM10. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.
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Affiliation(s)
- Srishti Jain
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Sudhir Kumar Sharma
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
| | - Narayanswami Vijayan
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India
| | - Tuhin Kumar Mandal
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India
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82
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Pratap V, Tiwari S, Kumar A, Singh AK. COVID-19 lockdown induced air pollution reduction over India: A lesson for future air pollution mitigation strategies. JOURNAL OF EARTH SYSTEM SCIENCE 2021. [PMCID: PMC8577420 DOI: 10.1007/s12040-021-01722-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Air pollution is one of the biggest problems worldwide and needs to be addressed potentially with the implementation of updated stringent policies and legislative laws. The nationwide lockdown imposed to prevent the COVID-19 outbreak, has given us a unique opportunity to understand the contribution of anthropogenic emissions to the total atmospheric pollutant burden on a global as well as regional scale. Thus, in the present study, we try to investigate the impact of COVID-19 induced lockdown on common ambient air pollutants (i.e., PM2.5, NO2, and SO2) concentration over 22 cities in India using in-situ measurement under a network of Centre Pollution and Control Board (CPCB). A significant reduction in the mean mass concentration of all the studied air pollutants (i.e., PM2.5, NO2, and SO2) (nearly 10–70%) is found during different phases of lockdown which reached within the National Ambient Air Quality Standard (i.e., NAAQS). The reduction in studied air pollutants is more prominent during the first phase of lockdown (mainly NO2) which could be due to the complete shutdown of industrial activities. The outcome of the present study will be helpful for policymakers to design cost-effective and accurate air pollution mitigation strategies for the development of a sustainable environment. The study also suggests that well-planned short-term and periodical lockdown could be an alternative effective tool of air pollution mitigation.
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Affiliation(s)
- Vineet Pratap
- Atmospheric Research Laboratory, Department of Physics, Banaras Hindu University, Varanasi, 221 005 India
| | - Shani Tiwari
- CSIR - National Institute of Oceanography, Dona Paula, Goa, 403 004 India
| | - Akhilesh Kumar
- Atmospheric Research Laboratory, Department of Physics, Banaras Hindu University, Varanasi, 221 005 India
| | - Abhay Kumar Singh
- Atmospheric Research Laboratory, Department of Physics, Banaras Hindu University, Varanasi, 221 005 India
- DST - Mahamana Centre of Excellence in Climate Change Research, Banaras Hindu University, Varanasi, 221 005 India
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83
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Singh V, Singh S, Biswal A. Exceedances and trends of particulate matter (PM 2.5) in five Indian megacities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 750:141461. [PMID: 32882489 PMCID: PMC7417276 DOI: 10.1016/j.scitotenv.2020.141461] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 08/01/2020] [Accepted: 08/01/2020] [Indexed: 05/04/2023]
Abstract
Fine particulate matter (PM2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m3 (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m3) suggest that not only is the annual mean PM2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.
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Affiliation(s)
- Vikas Singh
- National Atmospheric Research Laboratory, Gadanki, AP, India.
| | - Shweta Singh
- National Atmospheric Research Laboratory, Gadanki, AP, India
| | - Akash Biswal
- National Atmospheric Research Laboratory, Gadanki, AP, India
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84
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Goel V, Mishra SK, Pal P, Ahlawat A, Vijayan N, Jain S, Sharma C. Influence of chemical aging on physico-chemical properties of mineral dust particles: A case study of 2016 dust storms over Delhi. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 267:115338. [PMID: 32866861 DOI: 10.1016/j.envpol.2020.115338] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 06/27/2020] [Accepted: 07/29/2020] [Indexed: 05/17/2023]
Abstract
The physico-chemical properties of dust particles collected During Dust Storm (DDS) and After Dust Storm (ADS) events were studied using Scanning Electron Microscope coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), X-ray Fluorescence Spectroscopy (XRF) and X-ray Photoelectron Spectroscopy (XPS). Morphological and compositional change in dust particles were observed as they react with the anthropogenic pollutants present in the urban environment. The calcite rich particles were observed to transform into calcium chloride, calcium nitrate, and calcium sulfate on reacting with the chlorides, nitrates, and sulfates present in the urban atmosphere. The frequency distributions of Aspect Ratio (AR) for the DDS and ADS particles were observed to be bimodal (mode peaks at 1.2 and 1.5) and monomodal (mode peak at 1.1), respectively. The highly irregular shaped solid dust particles were observed to transform into nearly spherical semisolid particles in the urban environment. XPS analysis confirms the high concentration of oxides, nitrates, and chlorides at the surface of ADS samples which show the signatures of mineral dust particles aging. Species with a high value of imaginary part of refractive index (like Cr metal, Fe metal, Cr2O3, FeO, Fe2O3) were observed at the surface of dust particles. At 550 nm wavelength, the light-absorbing potential of the observed species along with black carbon (BC) was found to vary in the order; Cr metal > Fe metal > Cr2O3> FeO > BC > Fe2O3> FeOOH. The presence of the aforementioned species on the surface of ADS particles will tremendously affect the particle optical and radiative properties compared to that of DDS particles. The present work could reduce the uncertainty in the radiation budget estimations of mineral dust and assessment of their climatic impacts over Delhi.
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Affiliation(s)
- Vikas Goel
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India
| | - Sumit K Mishra
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India.
| | - Prabir Pal
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India; CSIR-Central Glass and Ceramic Research Institute, 196, Raja S. C. Mullick Road, Kolkata, 700032, India
| | - Ajit Ahlawat
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India; TROPOS, Leibniz Institute for Tropospheric Research, Permoserstraße, Leipzig, 04318, Germany
| | - Narayanasamy Vijayan
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India
| | - Srishti Jain
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India
| | - Chhemendra Sharma
- CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi, 110012, India; Academy of Scientific and Innovative Research (AcSIR), Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India
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85
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Comparison of ambient air pollution levels of Amritsar during foggy conditions with that of five major north Indian cities: multivariate analysis and air mass back trajectories. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03569-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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86
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Mishra PK, Bunkar N, Singh RD, Kumar R, Gupta PK, Tiwari R, Lodhi L, Bhargava A, Chaudhury K. Comparative profiling of epigenetic modifications among individuals living in different high and low air pollution zones: A pilot study from India.. [DOI: 10.1101/2020.09.15.20194928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
Abstract
AbstractEpigenetic modifications act as an important bridge to regulate the complex network of gene-environment interaction. As these mechanisms determines the gene-expression patterns via regulating the transcriptomic machinery, environmental stress induced epigenetic modifications may interrupt distinct cellular functions resulting into generation of diseased phenotypes. In the present study, we used a multi-city approach to compare the epigenomic signatures of individuals living in two tiers of Indian cities categorized as low-risk and high-risk air pollution zones. The high-risk group reported marked changes in the expression levels of epigenetic modifiers (DNMT1, DNMT3a, EZH2, EHMT2 and HAT), that maintains the levels of specific epigenetic marks essential for appropriate gene functioning. These results also coincided with the observed alterations in the levels of DNA methylation (LINE-1 and % 5mC), and histone modifications (H3 and H4), among the high-risk group. In addition, higher degree of changes reported in the expression profile of a selected miRNA panel in the high-risk group indicated the probability of deregulated transcriptional machinery. This was further confirmed by the analysis of a target gene panel involved in various signalling pathways, which revealed differential expression of the gene transcripts regulating cell cycle, inflammation, cell survival, apoptosis and cell adhesion. Together, our results provide first insights of epigenetic modifications among individuals living in different high and low levels of air pollution zones of India. However, further steps to develop a point-of-care epigenomic assay for human bio-monitoring may be immensely beneficial to reduce the health burden of air pollution especially in lower-middle-income countries.
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87
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Li Y, Liu B, Xue Z, Zhang Y, Sun X, Song C, Dai Q, Fu R, Tai Y, Gao J, Zheng Y, Feng Y. Chemical characteristics and source apportionment of PM 2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 263:114532. [PMID: 32311623 DOI: 10.1016/j.envpol.2020.114532] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 05/10/2023]
Abstract
The chemical species in PM2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM2.5 was 124 μg/m3 during the heating period, and NO3- and organic carbon (OC) were the dominant species. The concentrations and percentages of NO3-, SO42-, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.
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Affiliation(s)
- Yafei Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Baoshuang Liu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
| | - Zhigang Xue
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiaoyun Sun
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Congbo Song
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Ruichen Fu
- Linfen Eco-Environmental Bureau, Linfen, Shanxi, 041000, China
| | - Yonggang Tai
- Linfen Eco-Environmental Bureau, Linfen, Shanxi, 041000, China
| | - Jinyu Gao
- Linfen Eco-Environmental Bureau, Linfen, Shanxi, 041000, China
| | - Yajun Zheng
- Linfen Eco-Environmental Bureau, Linfen, Shanxi, 041000, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
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