A novel approach for assessing the spatiotemporal trend of health risk from ambient particulate matter components: Case of Hong Kong
Tài liệu tham khảo
Achilleos, 2017, Acute effects of fine particulate matter constituents on mortality: a systematic review and meta-regression analysis, Environ. Int., 109, 89, 10.1016/j.envint.2017.09.010
Agarwal, 2017, Characterization, sources and health risk analysis of PM2.5 bound metals during foggy and non-foggy days in sub-urban atmosphere of Agra, Atmos. Res., 197, 121, 10.1016/j.atmosres.2017.06.027
Badaloni, 2017, Effects of long-term exposure to particulate matter and metal components on mortality in the Rome longitudinal study, Environ. Int., 109, 146, 10.1016/j.envint.2017.09.005
Basagana, 2012, Effect of the number of measurement sites on land use regression models in estimating local air pollution, Atmos. Environ., 54, 634, 10.1016/j.atmosenv.2012.01.064
Bergen, 2013, A national prediction model for PM2.5 component exposures and measurement error–corrected health effect inference, Environ. Health Perspect., 121, 1017, 10.1289/ehp.1206010
Caplin, 2019, Advancing environmental exposure assessment science to benefit society, Nat. Commun., 10, 1, 10.1038/s41467-019-09155-4
Chen, 2020, Development of Europe-wide models for particle elemental composition using supervised linear regression and random forest, Environ. Sci. Technol., 54, 15698, 10.1021/acs.est.0c06595
Chung, 2015, Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States, Environ. Health Perspect., 123, 467, 10.1289/ehp.1307549
de Hoogh, 2013, Development of land use regression models for particle composition in twenty study areas in Europe, Environ. Sci. Technol., 47, 5778, 10.1021/es400156t
Dirgawati, 2016, Development of land use regression models for particulate matter and associated components in a low air pollutant concentration airshed, Atmos. Environ., 144, 69, 10.1016/j.atmosenv.2016.08.013
Emery, 2017, Recommendations on statistics and benchmarks to assess photochemical model performance, J. Air Waste Manag. Assoc., 67, 582, 10.1080/10962247.2016.1265027
Geng, 2020, Random forest models for PM2.5 speciation concentrations using MISR fractional AODs, Environ. Res. Lett., 15, 10.1088/1748-9326/ab76df
Gu, 2020, Assessing outdoor air quality and public health impact attributable to residential black carbon emissions in rural China, Resour. Conserv. Recycl., 159, 104812, 10.1016/j.resconrec.2020.104812
Hama, 2018, Chemical composition and source identification of PM10 in five North Western European cities, Atmos. Res., 214, 135, 10.1016/j.atmosres.2018.07.014
2020
Hou, 2019, Impacts of transboundary air pollution and local emissions on PM2.5 pollution in the Pearl River Delta region of China and the public health, and the policy implications, Environ. Res. Lett., 14, 10.1088/1748-9326/aaf493
Huang, 2018, In vitro bioaccessibility and health risk assessment of heavy metals in atmospheric particulate matters from three different functional areas of Shanghai, China, Sci. Total Environ., 610, 546, 10.1016/j.scitotenv.2017.08.074
Huang, 2017, Development of land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing, China, Environ. Res., 158, 542, 10.1016/j.envres.2017.07.010
Hvidtfeldt, 2021, Long-term exposure to fine particle elemental components and lung cancer incidence in the ELAPSE pooled cohort, Environ. Res., 193, 110568, 10.1016/j.envres.2020.110568
Kim, 2016, Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort, J. Expo. Environ. Epid., 26, 520, 10.1038/jes.2016.29
Lee, 2017, Land use regression modelling of air pollution in high density high rise cities: a case study in Hong Kong, Sci. Total Environ., 592, 306, 10.1016/j.scitotenv.2017.03.094
Li, 2019, Air pollution: a global problem needs local fixes, Nature, 570, 437, 10.1038/d41586-019-01960-7
Li, 2021, Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan, Atmos. Chem. Phys., 21, 5063, 10.5194/acp-21-5063-2021
Li, 2021, A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods, Chemosphere, 265, 129140, 10.1016/j.chemosphere.2020.129140
Li, 2020, High temporal resolution prediction of street-level PM2.5 and NOx concentrations using machine learning approach, J. Clean. Prod., 268, 121975, 10.1016/j.jclepro.2020.121975
Li, 2015, Characterization and source apportionment of health risks from ambient PM10 in Hong Kong over 2000–2011, Atmos. Environ., 122, 892, 10.1016/j.atmosenv.2015.06.025
Meng, 2016, Estimating ground-level PM10 in a Chinese city by combining satellite data, meteorological information and a land use regression model, Environ. Pollut., 208, 177, 10.1016/j.envpol.2015.09.042
Meng, 2018, Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005–2015, Environ. Int., 121, 1137, 10.1016/j.envint.2018.10.029
Nie, 2014, A 14-year measurement of toxic elements in atmospheric particulates in Hong Kong from 1995 to 2008, Front. Environ. Sci. Eng., 8, 553, 10.1007/s11783-013-0523-2
2015
Pande, 2018, Seasonal transition in PM10 exposure and associated all-cause mortality risks in India, Environ. Sci. Technol., 52, 8756, 10.1021/acs.est.8b00318
Pérez, 2007, Relations between PM10 composition and cell toxicity: a multivariate and graphical approach, Chemosphere, 67, 1218, 10.1016/j.chemosphere.2006.10.078
Ren, 2021, Bioaccessibility and public health risk of heavy metal(loid)s in the airborne particulate matter of four cities in Northern China, Chemosphere, 130312
Renzi, 2019, Long-term PM10 exposure and cause-specific mortality in the Latium Region (Italy): a difference-in-differences approach, Environ. Health Perspect., 127, 10.1289/EHP3759
Requia, 2019, Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space, Environ. Res., 175, 421, 10.1016/j.envres.2019.05.025
Saha, 2020, Spatial correlation of ultrafine particle number and fine particle mass at urban scales: implications for health assessment, Environ. Sci. Technol., 54, 9295, 10.1021/acs.est.0c02763
Sanchez, 2018, Development of land-use regression models for fine particles and black carbon in peri-urban South India, Sci. Total Environ., 634, 77, 10.1016/j.scitotenv.2018.03.308
Song, 2021, A machine learning approach to modelling the spatial variations in the daily fine particulate matter (PM2.5) and nitrogen dioxide (NO2) of Shanghai, China, Environ. Plan B Urban Anal. City Sci., 48, 467, 10.1177/2399808320975031
Sun, 2019, Respirable particulate constituents and risk of cause-specific mortality in the Hong Kong population, Environ. Sci. Technol., 53, 9810, 10.1021/acs.est.9b01635
Tian, 2019, Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics, Comput. Environ. Urban Syst., 75, 35, 10.1016/j.compenvurbsys.2019.01.003
Tripathy, 2019, Hybrid land use regression modeling for estimating spatio-temporal exposures to PM2.5, BC, and metal components across a metropolitan area of complex terrain and industrial sources, Sci. Total Environ., 673, 54, 10.1016/j.scitotenv.2019.03.453
Tsai, 2015, Spatial variation of PM elemental composition between and within 20 European study areas—results of the ESCAPE project, Environ. Int., 84, 181, 10.1016/j.envint.2015.04.015
2009
Vodonos, 2018, The concentration-response between long-term PM2.5 exposure and mortality; A meta-regression approach, Environ. Res., 166, 677, 10.1016/j.envres.2018.06.021
Weichenthal, 2018, Spatial variations in the estimated production of reactive oxygen species in the epithelial lung lining fluid by iron and copper in fine particulate air pollution, Environ. Epidemiol., 2, 10.1097/EE9.0000000000000020
Yim, 2012, Public health impacts of combustion emissions in the United Kingdom, Environ. Sci. Technol., 46, 4291, 10.1021/es2040416
Yim, 2014, An assessment indicator for air ventilation and pollutant dispersion potential in an urban canopy with complex natural terrain and significant wind variations, Atmos. Environ., 94, 297, 10.1016/j.atmosenv.2014.05.044
Yim, 2010, Use of high-resolution MM5/CALMET/CALPUFF system: SO2 apportionment to air quality in Hong Kong, Atmos. Environ., 44, 4850, 10.1016/j.atmosenv.2010.08.037
Yim, 2007, Developing a high‐resolution wind map for a complex terrain with a coupled MM5/CALMET system, J. Geophys. Res. Atmos., 112, D05106, 10.1029/2006JD007752
Yim, 2009, Air ventilation impacts of the “wall effect” resulting from the alignment of high-rise buildings, Atmos. Environ., 43, 4982, 10.1016/j.atmosenv.2009.07.002
Yim, 2019, Air quality and acid deposition impacts of local emissions and transboundary air pollution in Japan and South Korea, Atmos. Chem. Phys., 19, 13309, 10.5194/acp-19-13309-2019
Yim, 2015, Global, regional and local health impacts of civil aviation emissions, Environ. Res. Lett., 10, 10.1088/1748-9326/10/3/034001
Yim, 2013, Air quality and public health impacts of UK airports. Part II: impacts and policy assessment, Atmos. Environ., 67, 184, 10.1016/j.atmosenv.2012.10.017
Zhang, 2015, Development of land-use regression models for metals associated with airborne particulate matter in a North American city, Atmos. Environ., 106, 165, 10.1016/j.atmosenv.2015.01.008
Zheng, 2018, The effect of ambient particle matters on hospital admissions for cardiac arrhythmia: a multi-city case-crossover study in China, Environ. Health, 17, 60, 10.1186/s12940-018-0404-z