Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region

被引:12
作者
Chao, Chun-Ying [1 ,2 ]
Zhang, Huang [1 ,3 ]
Hammer, Melanie [4 ]
Zhan, Yu [5 ]
Kenney, David [1 ]
Martin, Randall, V [4 ]
Biswas, Pratim [1 ,6 ]
机构
[1] Washington Univ, Aerosol & Air Qual Res Lab, Dept Energy Environm & Chem Engn, St Louis, MO 63130 USA
[2] Rice Univ, Dept Civil & Environm Engn, Houston, TX 77005 USA
[3] Tsinghua Univ, Inst Nucl & New Energy Technol, Key Lab Adv Reactor Engn & Safety, Beijing 100084, Peoples R China
[4] Washington Univ, Dept Energy Environm & Chem Engn, Atmospher Composit Anal Grp, St Louis, MO 63130 USA
[5] Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Peoples R China
[6] Univ Miami, Coll Engn, Coral Gables, FL 33146 USA
来源
ACS EARTH AND SPACE CHEMISTRY | 2021年 / 5卷 / 11期
基金
美国国家科学基金会;
关键词
air quality; PM2.5; North China Plain; AOD; low-cost sensor; anthropogenic air pollution; Chinese New Year; AEROSOL OPTICAL DEPTH; PM2.5; CONCENTRATIONS; NEURAL-NETWORK; AOD; POLLUTION; URBAN; HAZE; EMISSIONS; MORTALITY; PRODUCTS;
D O I
10.1021/acsearthspacechem.1c00174
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
To address and remediate severe particulate matter (PM) pollution in the North China Plain (NCP), many studies have traced pollution sources by using fixed air quality monitoring stations. However, these fixed monitors have high maintenance costs that make it economically infeasible to construct spatially dense networks for air quality measurement. Alternatively, using satellite monitoring systems and a low-cost air quality sensor network can greatly increase the spatiotemporal resolution of the ground-level PM concentration data for a given region. This study comprehensively examines the performance of China's EPA monitoring stations (CN-EPA), low-cost PM sensor networks and satellite aerosol optical depth (AOD) measurements. The goal is to improve the spatiotemporal resolution of ground-level PM concentration data for Xinxiang, a typical industrial city in the NCP. The inferred results show that low-cost PM sensors demonstrate high linearity with CN-EPA data sets for PM,.. s concentrations with an R-2 value of 0.82. The PM2.5 concentration inferred from the AOD retrievals demonstrates a moderate correlation with fixed monitoring stations with an R-2 value of 0.53. To evaluate the impact of human activities on air pollution, four traditional Chinese festivals, Chinese New Year, Tomb Sweeping Day, Ghost Festival, and Moon Festival, are chosen to observe the PM distribution in Xinxiang. Heat-maps of the ground-level PM2.5 concentration reveal pollution hotspots in areas of high population density. Cross-validation is employed to evaluate the accuracy of the created pollution maps. The results demonstrate that pollution maps that were interpolated from data measured by CN-EPA data sets have the smallest root mean squared error (RMSE). Finally, our results show that low-cost PM sensor data can be integrated with traditional fixed air quality measurements to provide more detailed information about emission sources on pollution maps in urban and rural areas.
引用
收藏
页码:3022 / 3035
页数:14
相关论文
共 63 条
  • [1] Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes
    An, Zhisheng
    Huang, Ru-Jin
    Zhang, Renyi
    Tie, Xuexi
    Li, Guohui
    Cao, Junji
    Zhou, Weijian
    Shi, Zhengguo
    Han, Yongming
    Gu, Zhaolin
    Ji, Yuemeng
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2019, 116 (18) : 8657 - 8666
  • [2] High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data
    Apte, Joshua S.
    Messier, Kyle P.
    Gani, Shahzad
    Brauer, Michael
    Kirchstetter, Thomas W.
    Lunden, Melissa M.
    Marshall, Julian D.
    Portier, Christopher J.
    Vermeulen, Roel C. H.
    Hamburg, Steven P.
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2017, 51 (12) : 6999 - 7008
  • [3] Investigation of PM2.5 absorbed with heavy metal elements, source apportionment and their health impacts in residential houses in the North-east region of China
    Bai, Li
    He, Zijian
    Ni, Shenyang
    Chen, Wanyue
    Li, Na
    Sun, Siyue
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2019, 51
  • [4] Insights into measurements of ambient air PM2.5 in China
    Bai, Zhipeng
    Han, Jinbao
    Azzi, Merched
    [J]. TRENDS IN ENVIRONMENTAL ANALYTICAL CHEMISTRY, 2017, 13 : 1 - 9
  • [5] Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor
    Barkjohn, Karoline K.
    Gantt, Brett
    Clements, Andrea L.
    [J]. ATMOSPHERIC MEASUREMENT TECHNIQUES, 2021, 14 (06) : 4617 - 4637
  • [6] Evolution of PM2.5 Measurements and Standards in the US and Future Perspectives for China
    Cao, Junji
    Chow, Judith C.
    Lee, Frank S. C.
    Watson, John G.
    [J]. AEROSOL AND AIR QUALITY RESEARCH, 2013, 13 (04) : 1197 - 1211
  • [7] The air we breathe: differentials in global air quality monitoring
    Carvalho, Helotonio
    [J]. LANCET RESPIRATORY MEDICINE, 2016, 4 (08) : 603 - 605
  • [8] Chafe Z., 2015, Residential heating with wood and coal: health impacts and policy options in Europe and North America
  • [9] Assessment of population exposure to PM2.5 for mortality in China and its public health benefit based on BenMAP
    Chen, Li
    Shi, Mengshuang
    Gao, Shuang
    Li, Suhuan
    Mao, Jian
    Zhang, Hui
    Sun, Yanling
    Bai, Zhipeng
    Wang, Zhongliang
    [J]. ENVIRONMENTAL POLLUTION, 2017, 221 : 311 - 317
  • [10] Long-term exposure to urban air pollution and lung cancer mortality: A 12-year cohort study in Northern China
    Chen, Xi
    Zhang, Li-wen
    Huang, Jia-ju
    Song, Feng-ju
    Zhang, Luo-ping
    Qian, Zheng-min
    Trevathan, Edwin
    Mao, Hong-jun
    Han, Bin
    Vaughn, Michael
    Chen, Ke-xin
    Liu, Ya-min
    Chen, Jie
    Zhao, Bao-xin
    Jiang, Guo-hong
    Gu, Qing
    Bai, Zhi-peng
    Dong, Guang-hui
    Tang, Nai-jun
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 571 : 855 - 861