Spatial estimation of air PM2.5 emissions using activity data, local emission factors and land cover derived from satellite imagery

被引:3
|
作者
Gibe, Hezron P. [1 ]
Cayetano, Mylene G. [1 ,2 ]
机构
[1] Univ Philippines, Inst Environm Sci & Meteorol, Quezon City 1101, Philippines
[2] Gwangju Inst Sci & Technol, Int Environm Res Inst, Gwangju 500712, South Korea
关键词
PARTICULATE MATTER; POLLUTION;
D O I
10.5194/amt-10-3313-2017
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Exposure to particulate matter (PM) is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5) emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data) interpreted from satellite imagery. Geographic information system (GIS) software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal) and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.
引用
收藏
页码:3313 / 3323
页数:11
相关论文
共 50 条
  • [31] Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite
    Zhang, Ruijie
    Chen, Hui
    Chen, Ruizhi
    Zhou, Chunyan
    Li, Qing
    Xie, Huizhen
    Wang, Zhongting
    REMOTE SENSING, 2024, 16 (21)
  • [32] Improved retrieval of PM2.5 from satellite data products using non-linear methods
    Sorek-Hamer, M.
    Strawa, A. W.
    Chatfield, R. B.
    Esswein, R.
    Cohen, A.
    Broday, D. M.
    ENVIRONMENTAL POLLUTION, 2013, 182 : 417 - 423
  • [34] Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning
    Lee, Changsuk
    Lee, Kyunghwa
    Kim, Sangmin
    Yu, Jinhyeok
    Jeong, Seungtaek
    Yeom, Jongmin
    REMOTE SENSING, 2021, 13 (11)
  • [35] Combining Satellite-Derived PM2.5 Data and a Reduced-Form Air Quality Model to Support Air Quality Analysis in US Cities
    Gallagher, Ciaran L.
    Holloway, Tracey
    Tessum, Christopher W.
    Jackson, Clara M.
    Heck, Colleen
    GEOHEALTH, 2023, 7 (05):
  • [36] Estimation of PM2.5 and PM10 during an intense dust episode over Indian region using satellite data set.
    Tomar, Akshita
    Singh, Charu
    2023 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023, 2023,
  • [37] A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation
    Murray, Nancy L.
    Holmes, Heather A.
    Liu, Yang
    Chang, Howard H.
    ENVIRONMENTAL RESEARCH, 2019, 178
  • [38] Emission factors of metals bound with PM2.5 and ashes from biomass burning simulated in an open-system combustion chamber for estimation of open burning emissions
    Akbari, Mohammad Zahir
    Thepnuan, Duangduean
    Wiriya, Wan
    Janta, Rungruang
    Punsompong, Praphatsorn
    Hemwan, Phonpat
    Charoenpanyanet, Arisara
    Chantara, Somporn
    ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (03) : 13 - 24
  • [39] 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
    Wei-Bin Liao
    Ke Ju
    Qian Zhou
    Ya-Min Gao
    Jay Pan
    Environmental Science and Pollution Research, 2020, 27 : 22946 - 22955
  • [40] Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: Relationships to emissions and meteorological parameters
    Gui, Ke
    Che, Huizheng
    Wang, Yaqiang
    Wang, Hong
    Zhang, Lei
    Zhao, Hujia
    Zheng, Yu
    Sun, Tianze
    Zhang, Xiaoye
    ENVIRONMENTAL POLLUTION, 2019, 247 : 1125 - 1133