Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities

被引:17
|
作者
Henneman, Lucas R. F. [1 ]
Chang, Howard H. [2 ]
Liao, Kuo-Jen [3 ]
Lavoue, David [1 ]
Mulholland, James A. [1 ]
Russell, Armistead G. [1 ]
机构
[1] Georgia Inst Technol, Atlanta, GA 30332 USA
[2] Emory Univ, Atlanta, GA 30322 USA
[3] Texas A&M Univ Kingsville, Dept Environm Engn, Kingsville, TX USA
来源
AIR QUALITY ATMOSPHERE AND HEALTH | 2017年 / 10卷 / 06期
基金
美国国家科学基金会;
关键词
Air pollution modeling; Sensitivity; Accountability; Statistical modeling; AIR-QUALITY MODELS; EMISSIONS REDUCTIONS; SOURCE APPORTIONMENT; DYNAMIC EVALUATION; ORGANIC AEROSOL; UNITED-STATES; SURFACE OZONE; DISCOVER-AQ; HIGH-ORDER; TRENDS;
D O I
10.1007/s11869-017-0463-2
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Since the 1990 Clean Air Act Amendments, the USA has seen dramatic decreases in air pollutant emissions from a wide variety of source sectors, which have led to changes in pollutant concentrations: both up and down. Multiple stakeholders, including policy-makers, industry, and public health professionals, seek to quantify the benefits of regulations on air pollution and public health, a major focus of air pollution accountability research. Two methods, one empirical, the other based on a chemical transport model (CTM), are used to calculate the sensitivities of ozone (O-3) and particulate matter with diameters less than 2.5 mu m (PM2.5) to electricity-generating unit (EGU) and mobile source emissions. Both methods are applied to determine impacts of controls on daily concentrations (which are important in assessing acute health responses to air pollution), accounting for nonlinear, meteorologically, and emission-dependent responses of pollutant concentrations. The statistical method separates contributions of nearby EGU, regional EGU, and mobile source emissions on ambient city-center concentrations. Counterfactual emissions, an estimate of emissions under a scenario where no new controls were implemented on local EGU sources after 1995, regional EGUs after 1997, and mobile sources after 1993, are combined with these sensitivities to estimate counterfactual concentrations that represent what daily air quality in Atlanta, GA would have been had controls not been implemented and other emissions-reducing actions not been taken. Regulatory programs are linked with reduced peak summertime O-3, but have had little effect on annual median concentrations at the city-center monitoring site, and led to increases in pollutant levels under less photochemically-active conditions. The empirical method and the CTM method found similar relationships between ozone concentrations and ozone sensitivity to anthropogenic emissions. Compared to the counterfactual between 2010 and 2013, the number of days on which O-3 (PM2.5) concentrations exceeded 60 ppb (12.0 mu gm(-3)) was reduced from 396 to 200 (1391 to 222). In 2013, average daily ambient O-3 and PM2.5 concentrations were reduced by 1.0 ppb (2%) and 9.9 mu gm(-3) (48 %), respectively, and fourth highest maximum daily average 8-h O-3 was reduced by 14 ppb. Comparison of model-derived sensitivities to those derived using empirical methods show coherence, but some important differences, such as the O-3 concentration where the sensitivity to NOx emissions changes sign.
引用
收藏
页码:695 / 711
页数:17
相关论文
共 50 条
  • [1] Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities
    Lucas RF Henneman
    Howard H Chang
    Kuo-Jen Liao
    David Lavoué
    James A Mulholland
    Armistead G Russell
    Air Quality, Atmosphere & Health, 2017, 10 : 695 - 711
  • [2] Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions
    Agarwal, Akash
    Sahu, Manoranjan
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (04)
  • [3] Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions
    Akash Agarwal
    Manoranjan Sahu
    Environmental Monitoring and Assessment, 2023, 195 (4)
  • [4] Estimating PM2.5 concentrations with statistical distribution techniques for health risk assessment in Bangkok
    Thongthammachart, Tin
    Jinsart, Wanida
    HUMAN AND ECOLOGICAL RISK ASSESSMENT, 2020, 26 (07): : 1848 - 1863
  • [5] Sensitivities of Simulated Source Contributions and Health Impacts of PM2.5 to Aerosol Models
    Morino, Yu
    Ueda, Kayo
    Takami, Akinori
    Nagashima, Tatsuya
    Tanabe, Kiyoshi
    Sato, Kei
    Noguchi, Tadayoshi
    Ariga, Toshinori
    Matsuhashi, Keisuke
    Ohara, Toshimasa
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2017, 51 (24) : 14273 - 14282
  • [6] Assessing neighborhood variations in ozone and PM2.5 concentrations using decision tree method
    Gao, Ya
    Wang, Zhanyong
    Li, Chao-yang
    Zheng, Tie
    Peng, Zhong-Ren
    BUILDING AND ENVIRONMENT, 2021, 188
  • [7] Comparison of health and economic impacts of PM2.5 and ozone pollution in China
    Xie, Yang
    Dai, Hancheng
    Zhang, Yanxu
    Wu, Yazhen
    Hanaoka, Tatsuya
    Masui, Toshihiko
    ENVIRONMENT INTERNATIONAL, 2019, 130
  • [8] Spatiotemporal characteristics of PM2.5 and ozone concentrations in Chinese urban clusters
    Deng, Chuxiong
    Tian, Si
    Li, Zhongwu
    Li, Ke
    CHEMOSPHERE, 2022, 295
  • [9] Considerations in the use of ozone and PM2.5 data for exposure assessment
    Warren H. White
    Air Quality, Atmosphere & Health, 2009, 2 : 223 - 230
  • [10] Considerations in the use of ozone and PM2.5 data for exposure assessment
    White, Warren H.
    AIR QUALITY ATMOSPHERE AND HEALTH, 2009, 2 (04): : 223 - 230