Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration.
暂无分享,去创建一个
Jiayu Li | P. Biswas | Huang Zhang | Yu Zhan | Chun-Ying Chao | Qian-feng Liu | Chunying Wang | Shuangqing Jia | Lin Ma
[1] Alan M. Jones,et al. The wind speed dependence of the concentrations of airborne particulate matter and NOx , 2010 .
[2] Manuchehr Farajzadeh,et al. Modeling spatial distribution of Tehran air pollutants using geostatistical methods incorporate uncertainty maps , 2016 .
[3] Joel Schwartz,et al. Spatio-temporal modeling of chronic PM10 exposure for the Nurses' Health Study. , 2008, Atmospheric environment.
[4] Boštjan Gomišček,et al. On the equivalence of gravimetric PM data with TEOM and beta-attenuation measurements , 2004 .
[5] J. Douglas Faires,et al. Numerical Analysis , 1981 .
[6] F. Carrat,et al. Epidemiologic Mapping using the “Kriging” Method: Application to an Influenza-like Epidemic in France , 1992 .
[7] Michelle L Bell,et al. The use of ambient air quality modeling to estimate individual and population exposure for human health research: a case study of ozone in the Northern Georgia Region of the United States. , 2006, Environment international.
[8] A. Robinson,et al. Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation , 2018, Aerosol Science and Technology.
[9] S. Friedlander,et al. Smoke, dust, and haze , 2000 .
[10] Hans Wackernagel,et al. Multivariate Geostatistics: An Introduction with Applications , 1996 .
[11] Howard H. Chang,et al. Cross-comparison and evaluation of air pollution field estimation methods , 2018 .
[12] Jiayu Li,et al. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5 , 2020 .
[13] Yang Wang,et al. Laboratory Evaluation and Calibration of Three Low-Cost Particle Sensors for Particulate Matter Measurement , 2015 .
[14] Karina Gibert,et al. A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studies , 2019, Comput. Environ. Urban Syst..
[15] J. Gulliver,et al. A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .
[16] Yu Song,et al. A fast forecasting method for PM2.5 concentrations based on footprint modeling and emission optimization , 2019 .
[17] K. Sakamoto,et al. Examination of discrepancies between beta-attenuation and gravimetric methods for the monitoring of particulate matter , 2008 .
[18] Kebin He,et al. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China , 2016, Science Advances.
[19] S. Tao,et al. Improving regulations on residential emissions and non-criteria hazardous contaminants—Insights from a field campaign on ambient PM and PAHs in North China Plain , 2019, Environmental Science & Policy.
[20] Ronak Sutaria,et al. Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments , 2018, Atmospheric Measurement Techniques.
[21] Min Hu,et al. Using Low-cost sensors to Quantify the Effects of Air Filtration on Indoor and Personal Exposure Relevant PM2.5 Concentrations in Beijing, China , 2020 .
[22] Hyesop Shin,et al. Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities , 2014, Environmental health and toxicology.
[23] F. Liang,et al. Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China , 2014, Environmental Health.
[24] Ji-Young Son,et al. Individual exposure to air pollution and lung function in Korea: spatial analysis using multiple exposure approaches. , 2010, Environmental research.
[25] Chenyang Lu,et al. Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network , 2018 .
[26] M. L. Laucks,et al. Aerosol Technology Properties, Behavior, and Measurement of Airborne Particles , 2000 .
[27] Retrospective source attribution for source-oriented sampling , 2015 .
[28] Alexei Lyapustin,et al. Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements. , 2019, The Science of the total environment.
[29] P. Tchounwou,et al. Spatial Variation of Ground Level Ozone Concentrations and its Health Impacts in an Urban Area in India. , 2017, Aerosol and air quality research.
[30] William E. Wilson,et al. Measurement of total PM2.5 mass (nonvolatile plus semivolatile) with the Filter Dynamic Measurement System tapered element oscillating microbalance monitor , 2005 .
[31] Pratim Biswas,et al. Spatio‐temporal measurement of indoor particulate matter concentrations using a wireless network of low‐cost sensors in households using solid fuels , 2017, Environmental research.
[32] B. Chaix,et al. Does the air pollution model influence the evidence of socio‐economic disparities in exposure and susceptibility? , 2018, Environmental research.
[33] Yu Zhan,et al. Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm , 2017 .
[34] Ming Zhang,et al. Spatiotemporal patterns of aerosol optical depth throughout China from 2003 to 2016. , 2019, The Science of the total environment.
[35] Lester L. Yuan,et al. Comparison of spatial interpolation methods for the estimation of air quality data , 2004, Journal of Exposure Analysis and Environmental Epidemiology.
[36] R. E. Carter,et al. Effects of Wind Direction on Coarse and Fine Particulate Matter Concentrations in Southeast Kansas , 2006, Journal of the Air & Waste Management Association.
[37] Pratim Biswas,et al. Evaluation of Nine Low-cost-sensor-based Particulate Matter Monitors , 2020 .
[38] Elaine Symanski,et al. Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study , 2011, Environmental health : a global access science source.