Abstract
Assessments of spatiotemporal fine particulate matter (PM2.5) concentrations are crucial for establishing risk maps and maintaining human health. This study spatiotemporally assessed PM2.5 concentrations in Taiwan by using multivariate indicator kriging (MVIK) according to current Taiwanese and US regulatory standards for annual average PM2.5 concentrations (15 and 12 μg/m3, respectively). First, multivariate integration was implemented to analyze data on PM2.5 concentrations for 2019–2021 and 2020–2022 because of no statistical difference of the 3-year PM2.5 data. MVIK was then used for modeling probabilities according to the two standards. Finally, quantile estimates on the basis of the occurrence probabilities of analyzing PM2.5 concentrations were employed to determine the optimal classifications for establishing risk maps according to the two PM2.5 standards. The study results indicated that the multivariate integration of temporal PM2.5 data in MVIK can effectively streamline the analytic process. The multivariate integration of 3-year PM2.5 data was suitable for assessing the risk categories of the regulatory standards for annual average PM2.5. The greatest estimated difference between the 2019–2021 and 2020–2022 multivariate integrations was in the Northern and Chumiao air quality regions. Because many air quality regions were in the PM2.5 categories of exceeding 12 μg/m3, the regulatory standard for annual average PM2.5 of 12 μg/m3 was inappropriate in Taiwan at this point in time according to assessing the 3-year spatiotemporal variability of PM2.5 concentrations.
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References
Agarwal A, Kaushik A, Kumar S, Mishra RK (2020) Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air Qual Atmos Health 13:1167–1178. https://doi.org/10.1007/s11869-020-00881-z
Alexeeff SE, Schwartz J, Kloog I, Chudnovsky A, Koutrakis P, Coull BA (2015) Consequences of kriging and land use regression for PM25 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data. J Expos Sci Environ Epidemiol 25(2):138–144. https://doi.org/10.1016/j.envint.2022.107233
Cambardella CA, Moorman TB, Parkin TB, Karlen DL, Novak JM, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58:1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
Chan TC, Chou CC, Chu YC, Tang JH, Chen LC, Lin HH, Chen KJ, Chen RC (2022) Effectiveness of controlling COVID-19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan. Sci Rep 12(1):12053. https://doi.org/10.1038/s41598-022-16011-x
Chen Z, Chen D, Zhao C, Kwan M, Cai J, Zhuang Y, Zhao B, Wang X, Chen B, Yang J, Li R, He B, Gao B, Wang K, Xu B (2020) Influence of meteorological conditions on PM2.5 concentrations across China: a review of methodology and mechanism. Environ Int 139:105558. https://doi.org/10.1016/j.envint.2020.105558
Cheng FY, Hsu CH (2019) Long-term variations in PM2.5 concentrations under changing meteorological conditions in Taiwan. Sci Rep 9:6635. https://doi.org/10.1038/s41598-019-43104-x
Chilès JP, Delfiner P (1999) Geostatistics: modeling spatial uncertainty. John Wiley & Sons Inc., New York, pp 283–287
Choi G, Bell ML, Lee JT (2017) A study on modeling nitrogen dioxide concentrations using land-use regression and conventionally used exposure assessment methods. Environ Res Lett 12:044003. https://doi.org/10.1088/1748-9326/aa6057
Chu HJ, Ali MZ, He YC (2020) Spatial calibration and PM2.5 map** of low-cost air quality sensors. Sci Rep 10:22079. https://doi.org/10.1038/s41598-020-79064-w
Deutsch CV, Journel AG (1998) GSLIB: geostatistical software library and user’s guide, 2nd edn. Oxford University Press, New York
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, pp 259–368
Hsu CY, Chiang HC, Chen MJ, Chuang CY, Tsen CM, Fang GC, Tsai YI, Chen NT, Lin TY, Lin SL, Chen YC (2017) Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact. Sci Total Environ 590–591:204–214. https://doi.org/10.1016/j.scitotenv.2017.02.212
Hsu CY, Wu CD, Hsiao YP, Chen YC, Chen MJ, Lung CSC (2018) Develo** land-use regression models to estimate PM2.5-bound compound concentrations. Remote Sens 10(12):1971. https://doi.org/10.3390/rs10121971
Jang CS, Liu CW, Chou YL (2012a) Assessment of groundwater emergency utilization in Taipei basin during drought. J Hydrol 414–415:405–412. https://doi.org/10.1016/j.jhydrol.2011.11.016
Jang CS, Chen SK, Kuo YM (2012b) Establishing an irrigation management plan of sustainable groundwater based on spatial variability of water quality and quantity. J Hydrol 414–415:201–210. https://doi.org/10.1016/j.jhydrol.2011.10.032
Jang CS, Liang CP, Wang SW (2013) Integrating the spatial variability of water quality and quantity to probabilistically assess groundwater sustainability for use in aquaculture. Stoch Env Res Risk Assess 27:1281–1291. https://doi.org/10.1007/s00477-012-0664-z
Jang CS, Chen CF, Liang CP, Chen JS (2016) Combining groundwater quality analysis and a numerical flow simulation for spatially establishing utilization strategies for groundwater and surface water in the **tung plain. J Hydrol 533:541–556. https://doi.org/10.1016/j.jhydrol.2015.12.023
Jang CS, Kuo YM, Chen SK (2019) Assessment of shallow groundwater use for irrigating park trees in the metropolitan Taipei Basin according to variability conditions of water quality. J Hydrol X 2:100013. https://doi.org/10.1016/j.hydroa.2018.100013
Jang CS (2023) Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability. Environ Sci Pollut Res 30:81113–81130. https://doi.org/10.1007/s11356-023-28208-2
Kaufman JD, Adar SD, Barr RG, Budoff M, Burke GL, Curl CL, Daviglus ML, Diez Roux AV, Gassett AJ, Jacobs DR Jr, Kronmal R, Larson TV, Navas-Acien A, Olives C, Sampson PD, Sheppard L, Siscovick DS, Stein JH, Szpiro AA, Watson KE (2016) Association between air pollution and coronary artery calcification within six metropolitan areas in the USA (the multi-ethnic study of atherosclerosis and air pollution): a longitudinal cohort study. Lancet 388(10045):696–704. https://doi.org/10.1016/S0140-6736(16)00378-0
Kumar A, Mishra RK, Sarma K (2020) Map** spatial distribution of traffic induced criteria pollutants and associated health risks using kriging interpolation tool in Delhi. J Transp Health 18:100879. https://doi.org/10.1016/j.jth.2020.100879
Lai IC, Brimblecombe P (2021) Long-range transport of air pollutants to Taiwan during the COVID-19 lockdown in Hubei province. Aerosol Air Qual Res 21:200392. https://doi.org/10.4209/aaqr.2020.07.0392
Liao WB, Ju K, Zhou Q, Gao YM, Pan J (2020) Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study. Environ Sci Pollut Res 27:22946–22955. https://doi.org/10.1007/s11356-020-08843-9
Lin YC, Shih HS, Lai CY (2022) Classification of air quality zones and fine particulate matter sensitive areas by risk assessment approach. Environ Res 215:114208. https://doi.org/10.1016/j.envres.2022.114208
Lu HY, Wu YL, Mutuku JK, Chang KH (2019) Various sources of PM2.5 and their impact on the air quality in Tainan city. Taiwan Aerosol Air Qual Res 19:601–619. https://doi.org/10.4209/aaqr.2019.01.0024
Met One Instruments, Inc. (2019) BAM 1020 Continuous Particulate Monitor. Met One Instruments, Inc., Oregon, USA. https://metone.com/wp-content/uploads/2019/10/BAM-1020-4.pdf Accessed 10 April 2023
Mou CY, Hsu CY, Chen MJ, Chen YC (2021) Evaluation of variability in the ambient PM2.5 concentrations from FEM and FRM-like measurements for exposure estimates. Aerosol Air Qual Res 21:200217. https://doi.org/10.4209/aaqr.2020.05.0217
Nassikas NJ, Chan EAW, Nolte CG, Roman HA, Micklewhite N, Kinney PL, Carter EJ, Fann NL (2022) Modeling future asthma attributable to fine particulate matter (PM2.5) in a changing climate: a health impact assessment. Air Qual Atmos Health 15:311–319. https://doi.org/10.1007/s11869-022-01155-6
Saisana M, Dubois G, Chaloulakou A, Spyrellis N (2004) Classification criteria and probability risk maps: limitations and perspectives. Environ Sci Technol 38(5):1275–1281. https://doi.org/10.1021/es034652+
Shi L, Zanobetti A, Kloog I, Coull BA, Koutrakis P, Melly SJ, Schwartz JD (2016) Low-concentration PM2.5 and mortality: estimating acute and chronic effects in a population-based study. Environ Health Perspect 124(1):46–52. https://doi.org/10.1289/ehp.1409111
Smith JL, Halvorson JJ (2011) Field scale studies on the spatial variability of soil quality indicators in Washington state, USA. Appl Environ Soil Sci. https://doi.org/10.1155/2011/198737
Song R, Presto AA, Saha P, Zimmerman N, Ellis A, Subramanian R (2021) Spatial variations in urban air pollution: impacts of diesel bus traffic and restaurant cooking at small scales. Air Qual Atmos Health 14:2059–2072. https://doi.org/10.1007/s11869-021-01078-8
Taiwan’s Environmental Protection Administration (EPA) (2018) Air Quality Annual Report of R.O.C. (Taiwan) in 2018. Environmental Protection Administration, Executive Yuan, Taiwan, p 125
Taiwan’s Environmental Protection Administration (EPA) (2023) Taiwan Air Quality Monitoring Network. Environmental Protection Administration, Executive Yuan, Taiwan. https://airtw.epa.gov.tw/ENG/default.aspx Accessed 2 January 2023
US Environmental Protection Agency (2012) The National Ambient Air Quality Standards for Particle Pollution. Environmental Protection Agency, U.S. https://www.epa.gov/sites/default/files/2016-04/documents/2012_aqi_factsheet.pdf Accessed 24 April 2022
Wong YJ, Shiu HY, Chang JHH, Ooi MCG, Li HH, Homma R, Shimizu Y, Chiueh PT, Maneechot L, Sulaiman NMN (2022) Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: influence of urban public transportation use and meteorological conditions. J Clean Prod 365:132893. https://doi.org/10.1016/j.jclepro.2022.132893
Wu CF, Woodward A, Li YR, Kan H, Balasubramanian R, Latif MT, Sahani M, Cheng TJ, Chio CP, Taneepanichskul N, Kim H, Chan CC, Yi SM, Withers M, Samet J (2017a) Regulation of fine particulate matter (PM2.5) in the Pacific Rim: perspectives from the APRU global health program. Air Qual, Atmos Health 10(9):1039–1049. https://doi.org/10.1007/s11869-017-0492-x
Wu CD, Chen YC, Pan WC, Zeng YT, Chen MJ, Guo YL, Lung CSC (2017b) Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial–temporal variability. Environ Pollut 224:148–157. https://doi.org/10.1016/j.envpol.2017.01.074
Wu CD, Zeng YT, Lung CSC (2018) A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability. Sci Total Environ 645:1456–1464. https://doi.org/10.1016/j.scitotenv.2018.07.073
Yang D, Lu D, Xu J, Ye C, Zhao J, Tian G, Wang X, Zhu N (2018) Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze river Delta, China. Stoch Env Res Risk Assess 32:2445–2456. https://doi.org/10.1007/s00477-017-1497-6
Yassin MF, Al-Jazzaf AM, Shalash M (2021) GIS-based geostatistical approaches study on spatial-temporal distribution of ozone and its sources in hot, arid climates. Air Qual Atmos Health. https://doi.org/10.1007/s11869-021-01038-2
Yeh HC, Chen YC, Wei C (2020) Map** dust storm PM2.5 pollution risk using indicator kriging in northern Taiwan. Terr, Atmos Ocean Sci 31:313–323. https://doi.org/10.3319/TAO.2019.11.07.01
Zhang Z, Shan B, Lin Q, Chen Y, Yu X (2022) Influence of the spatial distribution pattern of buildings on the distribution of PM2.5 concentration. Stoch Env Res Risk Assess 36:985–997. https://doi.org/10.1007/s00477-021-02118-6
Acknowledgements
The author would like to thank the Taiwan Environmental Protection Administration generously supporting PM2.5 data in the website and the National Science and Technology Council, Taiwan for financially supporting this research under Contract No. MOST 109-2121-M-424-001.
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This work was supported by the National Science and Technology Council, Taiwan for financially supporting this research under Contract No. MOST 109-2121-M-424-001.
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Jang, CS. Probabilistic assessment of spatiotemporal fine particulate matter concentrations in Taiwan using multivariate indicator kriging. Stoch Environ Res Risk Assess 38, 761–776 (2024). https://doi.org/10.1007/s00477-023-02600-3
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DOI: https://doi.org/10.1007/s00477-023-02600-3