1
|
Katiyar A, Nayak DK, Nagar PK, Singh D, Sharma M, Kota SH. Fugitive road dust particulate matter emission inventory for India: A field campaign in 32 Indian cities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169232. [PMID: 38097065 DOI: 10.1016/j.scitotenv.2023.169232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
This research delves into the pivotal issue of road dust emissions and their profound ramifications on air quality across diverse regions of India. In pursuit of this objective, the study initiated a comprehensive field campaign to estimate silt loading (sL) values and evaluate the distribution of vehicles at 259 locations spanning 32 Indian cities. Remarkable disparities in sL values were observed across different road types and states. Notably, sites in Rajasthan, characterized by its arid Aravalli range and industrial activities, emerged as stark outliers, exhibiting significantly elevated sL values (up to 137 g/m2) compared to their counterparts. The regional analysis goes further to elucidate the relation between climatic conditions, topography, and silt loading. As a broader trend, roads in North India have higher sL values in contrast to those in South India. Further, a comprehensive particulate matter road dust emission inventory for the entire India in the year 2022 was developed using the vehicle registration data from 1352 road transport offices nationwide, in conjunction with the data from the field campaign concerning sL values and vehicle counts. Specific states such as Rajasthan, Uttar Pradesh, Maharashtra, Karnataka, and Gujarat emerged as the predominant contributors to road dust emissions. These states not only exhibit elevated sL values, but also account for a substantial proportion of the total registered vehicles in India, thereby underscoring the pressing imperative for effective mitigation measures. Weather Research and Forecasting coupled with chemistry (WRF-Chem) simulations, using this emission inventory, reveal that PM2.5 concentrations stemming from road dust exceed the World Health Organization guidelines in 55 % of the states across India. Further analysis delineates that more than 10,000 lives are annually lost due to PM2.5 pollution attributable to road dust in India, with the potential to salvage 10 % of these lives by paving all roads throughout the country.
Collapse
Affiliation(s)
- Arpit Katiyar
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Diljit Kumar Nayak
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Pavan Kumar Nagar
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Dhirendra Singh
- Airshed Planning Professionals Private Limited, Kanpur, India
| | - Mukesh Sharma
- Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Sri Harsha Kota
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
| |
Collapse
|
2
|
Dhandapani A, Iqbal J, Kumar RN. Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM 2.5 concentrations over India. CHEMOSPHERE 2023; 340:139966. [PMID: 37634588 DOI: 10.1016/j.chemosphere.2023.139966] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/31/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023]
Abstract
The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.
Collapse
Affiliation(s)
- Abisheg Dhandapani
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, Jharkhand, India
| | - Jawed Iqbal
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, Jharkhand, India
| | - R Naresh Kumar
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, Jharkhand, India.
| |
Collapse
|
3
|
Mezoue CA, Ngangmo YC, Choudhary A, Monkam D. Measurement of fine particle concentrations and estimation of air quality index (AQI) over northeast Douala, Cameroon. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:965. [PMID: 37462835 DOI: 10.1007/s10661-023-11582-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 07/03/2023] [Indexed: 07/21/2023]
Abstract
Due to absence of data on air quality monitoring and pollutant emissions in Douala, a measurement campaign along the principal street passage to the college grounds was started. Using the OC 300 Laser Dust Particle, fine particle concentrations are monitored during 1 week from Monday to Sunday. The instrument used detects four different sizes of particles: PM10, PM5, PM2.5, and PM1. The daily average concentrations measured ranged from 9.47 ± 0.26 to 50.14 ± 2.42 µg·m-3 for PM1.0; 13.13 ± 0.38 to 86.65 ± 3.96 µg·m-3 for PM2.5; 13.60 ± 0.40 to 100.56 ± 4.20 µg·m-3 for PM5; and 14.52 ± 0.42 to 114.59 ± 4.60 µg·m-3 for PM10. Exceptions made from PM5 and PM1.0 which were not in relation to the WHO (World Health Organization) guideline values, the level of PM10 and PM2.5 is higher than the WHO standards. The air quality index (AQI) is between very poor and poor during this measurement campaign, indicating that residents of the study region are highly exposed. Through the use of correlation studies, it has been demonstrated that the predominant source of fine particles in the studied region is vehicular activity. As a result, traffic density is the most significant factor causing the different air pollution levels seen in the tested areas.
Collapse
Affiliation(s)
- Cyrille Adiang Mezoue
- Faculty of Science, University of Douala, P.O. Box: 24157, Douala, Cameroon.
- National Higher Polytechnic School of Douala, P.O. Box: 2701, Douala, Cameroon.
| | | | - Arti Choudhary
- Centre of Environment Climate Change and Public Health, Utkal University, Bhubaneswar, Odisha, 751004, India
| | - David Monkam
- Faculty of Science, University of Douala, P.O. Box: 24157, Douala, Cameroon
| |
Collapse
|
4
|
Wang Y, Wang F, Min R, Song G, Song H, Zhai S, Xia H, Zhang H, Ru X. Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China. Sci Rep 2023; 13:8771. [PMID: 37253757 DOI: 10.1038/s41598-023-35399-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 05/17/2023] [Indexed: 06/01/2023] Open
Abstract
In this study, we simulated the spatial and temporal processes of a particulate matter (PM) pollution episode from December 10-29, 2019, in Zhengzhou, the provincial capital of Henan, China, which has a large population and severe PM pollution. As winter is the high incidence period of particulate pollution, winter statistical data were selected from the pollutant observation stations in the study area. During this period, the highest concentrations of PM2.5 (atmospheric PM with a diameter of less than 2.5 µm) and PM10 (atmospheric PM with a diameter of less than 10 µm) peaked at 283 μg m-3 and 316 μg m-3, respectively. The contribution rates of local and surrounding regional emissions within Henan (emissions from the regions to the south, northwest, and northeast of Zhengzhou) to PM concentrations in Zhengzhou were quantitatively analyzed based on the regional Weather Research and Forecasting model coupled with Chemistry (WRF/Chem). Model evaluation showed that the WRF/Chem can accurately simulate the spatial and temporal variations in the PM concentrations in Zhengzhou. We found that the anthropogenic emissions south of Zhengzhou were the main causes of high PM concentrations during the studied episode, with contribution rates of 14.39% and 16.34% to PM2.5 and PM10, respectively. The contributions of anthropogenic emissions from Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.94% and 7.29%, respectively. The contributions of anthropogenic emissions from the area northeast of Zhengzhou to the PM2.5 and PM10 concentrations in Zhengzhou were 7.42% and 7.18%, respectively. These two areas had similar contributions to PM pollution in Zhengzhou. The area northeast of Zhengzhou had the lowest contributions to the PM2.5 and PM10 concentrations in Zhengzhou (5.96% and 5.40%, respectively).
Collapse
Affiliation(s)
- Yaobin Wang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Feng Wang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Ruiqi Min
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Genxin Song
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China.
| | - Hongquan Song
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China.
- Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng, 475004, Henan, China.
| | - Shiyan Zhai
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Haoming Xia
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
| | - Haopeng Zhang
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| | - Xutong Ru
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan, China
- Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, Henan, China
| |
Collapse
|
5
|
Choi H, Park S, Kang Y, Im J, Song S. Retrieval of hourly PM 2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121169. [PMID: 36773685 DOI: 10.1016/j.envpol.2023.121169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/11/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 μm (PM2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolutions. Three ML techniques were used to estimate ground-level PM2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI-II-based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 μg/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 μg/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI-II-based models produced a more detailed spatial distribution of PM2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM2.5 concentrations for operational purposes.
Collapse
Affiliation(s)
- Hyunyoung Choi
- Department of Urban Environment Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Seonyoung Park
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
| | - Yoojin Kang
- Department of Urban Environment Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Jungho Im
- Department of Urban Environment Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea; Research & Management Center for Particulate Matters at the Southeast Region of Korea, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, South Korea.
| | - Sanghyeon Song
- Department of Urban Environment Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| |
Collapse
|
6
|
Kamińska JA, Kajewska-Szkudlarek J. The importance of data splitting in combined NO x concentration modelling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 868:161744. [PMID: 36690101 DOI: 10.1016/j.scitotenv.2023.161744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 06/17/2023]
Abstract
The polluted air breathed every day by those living in large conurbations poses a significant risk to their health. Through effective modelling (prediction) of concentrations of pollutants and identification of the factors influencing them, it should be possible to obtain advance information on dangers and to plan and implement measures to reduce them. This work describes two different modelling approaches: based on the NOx concentration of the previous hour (C&RT models); and based on meteorological factors, traffic flow, and past (up to two previous hours) NOx and NO2 concentrations (CA models). For each approach, three alternative machine learning methods were applied: artificial neutral network (ANN), random forest (RF), and support vector regression (SVR). The best fits were obtained for the models using ANN and RF (MAPE values in the range 18.3-18.5 %). Poorer fits were found for the SVR models (MAPE equal to 23.4 % for the C&RT approach and 29.3 % for CA). No significant preferences were identified between the C&RT and CA approaches (based on various goodness-of-fit measures). The choice should be determined by the purposes for which the forecast is to be used.
Collapse
Affiliation(s)
- Joanna A Kamińska
- Department of Applied Mathematics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka Street 53, 50-357 Wroclaw, Poland
| | - Joanna Kajewska-Szkudlarek
- Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, Grunwaldzki Square 24, 50-363 Wroclaw, Poland.
| |
Collapse
|
7
|
Quan W, Xia N, Guo Y, Hai W, Song J, Zhang B. PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China. PLoS One 2023; 18:e0285610. [PMID: 37167212 PMCID: PMC10174561 DOI: 10.1371/journal.pone.0285610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/26/2023] [Indexed: 05/13/2023] Open
Abstract
PM2.5 is closely linked to both air quality and public health. Many studies have used models combined with remote sensing and auxiliary data to inverse a large range of PM2.5 concentrations. However, the data's spatial resolution is limited. and better results might have been obtained if higher resolution data had been used. Therefore, this paper establishes a geographical and temporal weighted regression model (GTWR) and estimates the PM2.5 concentration in Xinjiang from 2015 to 2020 using 1 km resolution MCD19A2 (MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006) data and 9 auxiliary variables. The findings indicate that the GTWR model performs better than the simple linear regression (SLR) and geographically weighted regression (GWR) models in terms of accuracy and feasibility in retrieving PM2.5 concentrations in Xinjiang. Simultaneously, by combining the GTWR model with MCD19A2 data, a spatial distribution map of PM2.5 with better spatial resolution can be obtained. Next, the regional distribution of annual PM2.5 concentrations in Xinjiang is consistent with the terrain from 2015 to 2020. The low value area is primarily found in the mountainous area with higher terrain, while the high value area is primarily in the basin with lower terrain. Overall, the southwest is high and the northeast is low. In terms of time change, the six-year PM2.5 shows a single peak distribution with 2016 as the inflection point. Lastly, from 2015 to 2020, the seasonal average PM2.5 concentration in Xinjiang has a significant difference, thereby showing winter (66.15μg/m3)>spring (52.28μg/m3)>autumn (40.51μg/m3)>summer (38.63μg/m3). The research shows that the combination of MCD19A2 data and GTWR model has good applicability in retrieving PM2.5 concentration.
Collapse
Affiliation(s)
- Weilin Quan
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, MNR, Urumqi, China
| | - Nan Xia
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, MNR, Urumqi, China
| | - Yitu Guo
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
| | - Wenyue Hai
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, MNR, Urumqi, China
| | - Jimi Song
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, MNR, Urumqi, China
| | - Bowen Zhang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
- Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, MNR, Urumqi, China
| |
Collapse
|
8
|
Chen Y, Li D, Karimian H, Wang S, Fang S. The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta. CHEMOSPHERE 2022; 308:136301. [PMID: 36064028 DOI: 10.1016/j.chemosphere.2022.136301] [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: 05/15/2022] [Revised: 08/18/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R2 ˃ 0.57), summer (R2 ˃ 0.76) and autumn (R2 ˃ 0.38). However, in winter the composite model outperformed others (R2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41<R2 <0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.
Collapse
Affiliation(s)
- Youliang Chen
- Department of Geo-informatics, Central South University, Changsha, 410000, China; School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Dan Li
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Hamed Karimian
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.
| | - Shiteng Wang
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Shuwei Fang
- Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, China
| |
Collapse
|
9
|
Raju L, Gandhimathi R, Mathew A, Ramesh S. Spatio-temporal modelling of particulate matter concentrations using satellite derived aerosol optical depth over coastal region of Chennai in India. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
10
|
Ma X, Ding Y, Shi H, Yan W, Dou X, Ochege FU, Luo G, Zhao C. Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 816:151558. [PMID: 34762952 DOI: 10.1016/j.scitotenv.2021.151558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 10/22/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.
Collapse
Affiliation(s)
- Xiaofei Ma
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent, Belgium and Urumqi, China.
| | - Yu Ding
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyang Shi
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Department of Geography, Ghent University, Ghent 9000, Belgium; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Yan
- School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
| | - Xin Dou
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Friday Uchenna Ochege
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent, Belgium and Urumqi, China
| | - Geping Luo
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent, Belgium and Urumqi, China
| | - Chengyi Zhao
- School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
| |
Collapse
|
11
|
Jin X, Ding J, Ge X, Liu J, Xie B, Zhao S, Zhao Q. Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions. PeerJ 2022; 10:e13203. [PMID: 35378927 PMCID: PMC8976473 DOI: 10.7717/peerj.13203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/10/2022] [Indexed: 01/12/2023] Open
Abstract
PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015-2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m-3) > spring (64.76 µg m-3) > autumn (46.01 µg m-3) > summer (43.40 µg m-3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.
Collapse
Affiliation(s)
- XiaoYe Jin
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| | - Jianli Ding
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China,MNR Technology Innovation Center for Central Asia Geo-Information Exploitation and Utilization, Urumqi, China
| | - Xiangyu Ge
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| | - Jie Liu
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| | - Boqiang Xie
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| | - Shuang Zhao
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| | - Qiaozhen Zhao
- Department of MOE Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China,College of Resources and Environment Science, Xinjiang University, Urumqi, China
| |
Collapse
|
12
|
Wen J, Chuai X, Gao R, Pang B. Regional interaction of lung cancer incidence influenced by PM 2.5 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:149979. [PMID: 34487906 DOI: 10.1016/j.scitotenv.2021.149979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/05/2021] [Accepted: 08/24/2021] [Indexed: 05/16/2023]
Abstract
PM2.5 is the key pollutant threatening human health and can even cause lung cancer. Pollution is the most serious problem in China with its fast industrialisation, urbanisation and high population density. This pollutant is conveyed through the atmosphere, trade and the embodied emission flow amongst regions. Scientific evaluation of the responsibility for regional lung cancer by considering both internal and external influences seems to be meaningful in addressing regional inequity. This study develops a relatively convenient and practical method to evaluate the regional inequity reflected by lung cancer associated with PM2.5 pollution in China. Results show that PM2.5 emissions and concentrations have similar distribution patterns: high values were predominant in the east and south where has high population density, while the west had low values. The cancer incidence rate showed high values mainly in eastern and central China. At a provincial scale, the lung cancer incidence rate was significantly correlated with PM2.5 concentration levels, and a high correlation was also found between PM2.5 concentration and emissions, indicating that emission reduction is the key to lung cancer prevention. Due to domestic trade, some developed regions more pulled lung cancer in less developed regions, and some less developed regions also have an obvious influence on external regions. Spatially, provinces in northern and central China are always more influenced by external regions. Lung cancer inequity analysis shows that coastline regions are more advantaged, while the reverse applies to inland China. The central government needs to further strengthen regional coordinated development measures, such as economic compensation for medical care and adjustments to industry structure. It should optimise spatial allocation and comprehensively consider regional inequity and character.
Collapse
Affiliation(s)
- Jiqun Wen
- School of Public Administration, Guangdong University of Finance and Economics, Guangzhou 510320, Guangdong Province, China
| | - Xiaowei Chuai
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China.
| | - Runyi Gao
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| | - Baoxin Pang
- Department of Philosophy, Nanjing University, Nanjing 210023, Jiangsu Province, China; School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| |
Collapse
|
13
|
Wang J, He L, Lu X, Zhou L, Tang H, Yan Y, Ma W. A full-coverage estimation of PM 2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China. ENVIRONMENTAL RESEARCH 2022; 203:111799. [PMID: 34343552 DOI: 10.1016/j.envres.2021.111799] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.
Collapse
Affiliation(s)
- Jiajia Wang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, 200433, China
| | - Li He
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Xiaoman Lu
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai, 200062, China
| | - Liguo Zhou
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, 200433, China; Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai, 200062, China.
| | - Haoyue Tang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Yingting Yan
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Weichun Ma
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China; Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, 200433, China.
| |
Collapse
|
14
|
Jalali S, Karbakhsh M, Momeni M, Taheri M, Amini S, Mansourian M, Sarrafzadegan N. Long-term exposure to PM 2.5 and cardiovascular disease incidence and mortality in an Eastern Mediterranean country: findings based on a 15-year cohort study. Environ Health 2021; 20:112. [PMID: 34711250 PMCID: PMC8555193 DOI: 10.1186/s12940-021-00797-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 10/17/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Evidence concerning the impact of long-term exposure to fine Particulate Matter ≤2.5 μm (PM2.5) on Cardio-Vascular Diseases (CVDs) for those people subject to ambient air pollution in developing countries remains relatively scant. This study assessed the relationship of 15-year PM2.5 exposure with cardiovascular incidence and mortality rate in Isfahan province, Iran. METHODS The cohort comprised 3081 participants over 35 years old who were free of CVDs. They were selected through multi-stage cluster sampling in Isfahan, Iran. PM2.5 exposure was determined separately for each individual via satellite-based spatiotemporal estimates according to their residential addresses. In this context, CVD is defined as either fatal and non-fatal Acute Myocardial Infarctions (AMI) or stroke and sudden cardiac death. The incidence risk for CVD and the ensuing mortality was calculated based on the average PM2.5 exposure within a study period of 15 years using the Cox proportional hazards frailty model upon adjusting individual risk factors. The mean annual rate of PM2.5 and the follow-up data of each residential area were combined. RESULTS Mean three-year PM2·5 exposure for the cohort was measured at 45.28 μg/m3, ranging from 20.01 to 69.80 μg/m3. The median time period for conducting necessary follow-ups was 12.3 years for the whole population. Notably, 105 cardiovascular and 241 all-cause deaths occurred among 393,786 person-months (27 and 61 per 100,000 person-months, respectively). In well-adjusted models, 10 μg/m3 increase in PM2.5 corresponded to a 3% increase in the incidence rate of CVDs [0.95 CI = 1.016, 1.036] (in case of p = 0.000001 per 10 μg/m3 increase in PM2.5, the Hazard Ratio (HR) for AMI and Ischemic Heart Disease (IHD) was 1.031 [0.95 CI = 1.005, 1.057] and 1.028 [0.95 CI = 1.017, 1.039]), respectively. No consistent association was observed between PM2.5 concentration and fatal CVD (fatal AMI, fatal stroke, SCD (Sudden Cardiac Death)) and all-cause mortality. CONCLUSIONS Results from analyses suggest that the effect of PM2.5 on cardiovascular disease occurrence was stronger in the case of older people, smokers, and those with high blood pressure and diabetes. The final results revealed that long-term exposure to ambient PM2.5 with high concentrations positively correlated with IHD incidence and its major subtypes, except for mortality. The outcome accentuates the need for better air quality in many countries.
Collapse
Affiliation(s)
- Soheila Jalali
- Student Research Committee, Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mojgan Karbakhsh
- Department of Community and Preventive Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Momeni
- Department of Surveying Engineering, University of Isfahan, Isfahan, Iran
| | - Marzieh Taheri
- Pediatric Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeid Amini
- Department of Surveying and Geomatics Engineering, University of Isfahan, Isfahan, Iran
| | - Marjan Mansourian
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
- Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Building H, Floor 4, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
- School of Population & Public Health, University of British Columbia, Vancouver, Canada
| |
Collapse
|
15
|
Sharma S, Sharma R, Sahu SK, Kota SH. Transboundary sources dominated PM 2.5 in Thimphu, Bhutan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY : IJEST 2021; 19:5649-5658. [PMID: 34226828 PMCID: PMC8243619 DOI: 10.1007/s13762-021-03505-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 06/02/2021] [Accepted: 06/22/2021] [Indexed: 06/13/2023]
Abstract
This study estimates the potential source regions contributing to PM2.5 in the capital city of Thimphu, Bhutan, during the years 2018-2020 using the ground-based data, followed by the HYSPLIT back trajectory analysis. The average PM2.5 concentration in the entire study period was 32.47 µg/m3 which is three times of the World Health Organization recommended limit of 10 µg/m3. Less than half of the days in pre-monsoon (43.47%) and post-monsoon (46.41%), and no days in winter were within the 24-h average WHO guideline of 25 μg/m3. During the COVID-19 lockdown imposed from August 11 to September 21 in Bhutan, only a marginal reduction of 4% in the PM2.5 concentrations was observed, indicating that nonlocal emissions dominate the PM2.5 concentrations in Thimphu, Bhutan. Most back trajectories in the analysis period were allocated to south or south-west sector. India was the major contributor (~ 44%), followed by Bangladesh (~ 19%), Bhutan itself (~ 19%) and China (~ 16%). This study confirms that there are significant contributions from transboundary sources to PM2.5 concentrations in Thimphu, Bhutan, and the elevated PM2.5 concentrations need to be tackled with appropriate action plans and interventions.
Collapse
Affiliation(s)
- S. Sharma
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India
| | - R. Sharma
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India
| | - S. K. Sahu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084 China
| | - S. H. Kota
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016 India
| |
Collapse
|
16
|
Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015. ENERGIES 2021. [DOI: 10.3390/en14113232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.
Collapse
|
17
|
Population exposure across central India to PM 2.5 derived using remotely sensed products in a three-stage statistical model. Sci Rep 2021; 11:544. [PMID: 33436655 PMCID: PMC7804491 DOI: 10.1038/s41598-020-79229-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/07/2020] [Indexed: 11/08/2022] Open
Abstract
Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled and observed PM2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m-3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM2.5 concentration (mean value 82.54 µg m-3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m-3). Our results show that MP had a mean PM2.5 concentration of 58.19 µg m-3 and 56.32 µg m-3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period.
Collapse
|
18
|
Abstract
In November 2019, the Supreme Court of India issued a notification to all the states in the National Capital Region of Delhi to install smog towers for clean air and allocated INR 36 crores (~USD 5.2 million) for a pilot. Can we vacuum our air pollution problem using smog towers? The short answer is “no”. Atmospheric science defines the air pollution problem as (a) a dynamic situation where the air is moving at various speeds with no boundaries and (b) a complex mixture of chemical compounds constantly forming and transforming into other compounds. With no boundaries, it is unscientific to assume that one can trap air, clean it, and release into the same atmosphere simultaneously. In this paper, we outline the basics of atmospheric science to describe why the idea of vacuuming outdoor air pollution is unrealistic, and the long view on air quality management in Indian cities.
Collapse
|