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Wang B, Wang N, Chen Z. Research on air quality forecast based on web text sentiment analysis. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li J, He Q, Ge X, Abbas A, Jin L. Spatio-temporal changes of AOD in Xinjiang of China from 2000 to 2019: Which factor is more influential, natural factor or human factor? PLoS One 2021; 16:e0253942. [PMID: 34411113 PMCID: PMC8376058 DOI: 10.1371/journal.pone.0253942] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/15/2021] [Indexed: 12/13/2022] Open
Abstract
Aerosol optical depth (AOD), which represents the optical attenuation, poses a major threat to the production activity, air quality, human health and regional sustainable development of arid and semi-arid areas. To some degree, AOD shows areal air pollution level and possesses obvious spatio-temporal characteristics. However, long-time sequences and detailed AOD information can not be provided due to currently limited monitoring technology. In this paper, a daily AOD product, MODIS-based Multi-angle Implementation of Atmospheric Correction (MAIAC), is deployed to analyze the spatio-temporal characteristics in Xinjiang Uygur Autonomous Region from 2000 to 2019. In addition, the importance of influencing factors for AOD is calculated through Random Forest (RF) Model and the propagation trajectories of pollutants are simulated through Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. Spatio distribution of AOD presents a tendency that AOD value in northern Xinjiang is low while the value in southern Xinjiang is high. Regions with high AOD values are mainly concentrated in Tarim Basin. AOD in southern Xinjiang is the highest, followed by that in eastern Xinjiang and AOD value in northern Xinjiang is the lowest. Seasonal variation of AOD is significant: Spring (0.309) > summer (0.200) > autumn (0.161) > winter (0.158). Average AOD value in Xinjiang is 0.196. AOD appears wavy from 2000 to 2014 with its low inflection point (0.157) appearing in 2005, and then increases, reaching its peak in 2014 (0.223). The obvious downward tendency after 2014 shows that the use of coal to natural gas (NG) conversion project improves the conditions of local environment. According to RF Model, NG contributes most to AOD. HYSPLIT Model reveals that aerosol in southern Xinjiang is related to the short-distant carriage of dust aerosol from the Taklimakan Desert. Aerosol there can affect Inner Mongolia through long-distant transport. Blocked by the Tianshan Mountains, fine dust particles can not cross the Tianshan Mountains to become a factor contributing to AOD in northern Xinjiang.
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Affiliation(s)
- Jinglong Li
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, Xinjiang, China
| | - Qing He
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, Xinjiang, China
| | - Xiangyu Ge
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China.,Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, Xinjiang, China.,Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China
| | - Alim Abbas
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, Xinjiang, China
| | - Lili Jin
- Department of Atmospheric Sciences, Yunnan University, Kunming, China
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Li J, Ge X, He Q, Abbas A. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China. PeerJ 2021; 9:e10542. [PMID: 33505790 PMCID: PMC7792517 DOI: 10.7717/peerj.10542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 11/20/2020] [Indexed: 12/29/2022] Open
Abstract
Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.
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Affiliation(s)
- Jinglong Li
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
| | - Xiangyu Ge
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, China.,Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China.,Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi, Xinjiang, China
| | - Qing He
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
| | - Alim Abbas
- College of Resources and Environment Sciences, Xinjiang University, Urumqi, China.,Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China
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Vu BN, Sánchez O, Bi J, Xiao Q, Hansel NN, Checkley W, Gonzales GF, Steenland K, Liu Y. Developing an Advanced PM 2.5 Exposure Model in Lima, Peru. REMOTE SENSING 2019; 11. [PMID: 31372305 PMCID: PMC6671674 DOI: 10.3390/rs11060641] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.
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Affiliation(s)
- Bryan N. Vu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Odón Sánchez
- Carrera Profesional de Ingeniería Ambiental, Universidad Nacional Tecnológica de Lima Sur (UNTELS), cruce Av. Central y Av. Bolivar, Villa El Salvador, Lima 15102, Peru
| | - Jianzhao Bi
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Qingyang Xiao
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Nadia N. Hansel
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - William Checkley
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Gustavo F. Gonzales
- Endocrinology and Reproduction Unit, Research and Development Laboratories (LID), Faculty of Sciences and Philosophy, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Department of Biological and Physiological Sciences, Faculty of Sciences and Philosophy, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Instituto de Investigaciones de la Altura, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Kyle Steenland
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Yang Liu
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
- Correspondence:
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