Predicting differential improvements in annual pollutant concentrations and exposures for regulatory policy assessment

• Published in: Environment international Vol. 143; p. 105942
• Main Authors: Su, Jason G., Meng, Ying-Ying, Chen, Xiao, Molitor, John, Yue, Dahai, Jerrett, Michael
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.10.2020; Elsevier
• Subjects: Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Air Pollution - prevention & control; Animals; Deletion/substitution/addition; Difference-in-difference; Emissions reduction plan; Environmental Monitoring; Environmental Pollutants; Humans; Land use regression; Nitrogen dioxide; Nitrogen Dioxide - analysis; Particulate Matter - analysis; Policy; Rabbits; Deletion/substitution/addition; Land use regression; Air pollution; Difference-in-difference; Nitrogen dioxide; Emissions reduction plan
• ISSN: 0160-4120; 1873-6750
• DOI: 10.1016/j.envint.2020.105942

•Integrate multiple types of measures of an air pollutant into one single modeling framework.•Deal with repeated measures for annual air pollution model development.•Difference-in-difference analysis to identify differential improvements in air pollution exposure. Over the past decade, researchers and policy-makers have become increasingly interested in regulatory and policy interventions to reduce air pollution concentrations and improve human health. Studies have typically relied on relatively sparse environmental monitoring data that lack the spatial resolution to assess small-area improvements in air quality and health. Few studies have integrated multiple types of measures of an air pollutant into one single modeling framework that combines spatially- and temporally-rich monitoring data. In this paper, we investigated the differential effects of California emissions reduction plan on reducing air pollution between those living in the goods movement corridors (GMC) that are within 500 m of major highways that serve as truck routes to those farther away or adjacent to routes that prohibit trucks. A mixed effects Deletion/Substitution/Addition (D/S/A) machine learning algorithm was developed to model annual pollutant concentrations of nitrogen dioxide (NO2) by taking repeated measures into consideration and by integrating multiple types of NO2 measurements, including those through government regulatory and research-oriented saturation monitoring into a single modeling framework. Difference-in-difference analysis was conducted to identify whether those living in GMC demonstrated statistically larger reductions in air pollution exposure. The mixed effects D/S/A machine learning modeling result indicated that GMC had 2 ppb greater reductions in NO2 concentrations from pre- to post-policy period than far away areas. The difference-in-difference analysis demonstrated that the subjects living in GMC experienced statistically significant greater reductions in NO2 exposure than those living in the far away areas. This study contributes to scientific knowledge by providing empirical evidence that improvements in air quality via the emissions reductions plan policies impacted traffic-related air pollutant concentrations and associated exposures most among low-income Californians with chronic conditions living in GMC. The identified differences in pollutant reductions across different location domains may be applicable to other states or other countries if similar policies are enacted.