[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126961-en":3,"doc-seo-126961-105":30,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126961,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Evaluation of pollution containment policies in the US and the role of machine learning algorithms","The study analyzes policy actions and institutional changes in local governance structures as drivers of air pollutant reductions in US urban areas. A dataset is built on traffic-related air pollution and socio-economic characteristics across US urbanized areas, drawing from Google Earth Engine and institutional sources. Raw inputs are produced using machine-learning applied to satellite imagery and monitoring-station records at multiple spatiotemporal resolutions. Regression discontinuity design is then used to evaluate pollution-reduction policies leveraging US Transport Management Areas as a quasi-experimental framework.","Evaluation of pollution containment policies in the US and the role of machine learning algorithms  \nMargherita Gerolimettoa and Stefano Magrinia  \naCa’ Foscari Univesity Venice; marco .dicataldo@unive .it, margherita.gerolimetto@unive.it, [stefano.magrini@unive.it](stefano.magrini@unive.it), [alessandro.spiganti@unive.it](alessandro.spiganti@unive.it)  \nAbstract  \nThe aim of this study is to analyse policy actions and institutional changes in local governance structures as determinants of air pollutant reductions in US urban areas. First, we construct a dataset on traffic-related air pollution and socio-economic characteristics across urbanized areas of the US. Some of these data are available through Google Earth engine, others are instead provided by institutional sources. In general, raw data come from application of machine learning techniques to either satellite images or monitoring station records and are available at different temporal and spatial resolutions. Then we adopt regression discontinuity design techniques for the evaluations of pollution reduction policies, exploiting the designation of US Transport Management Areas as a quasi-experimental framework.  \nKeywords: air pollution, policy evaluation, regression discontinuity design, machine learning  \n1. Introduction  \nRoad traffic is one of the main contributors to air pollution and greenhouse gasses around the world (among others McDuffie et al., 2021) . The mixture of vehicle exhausts from fuel combustion and nonexhaust from engine, brake, tire, and road surface wear and re-suspended street dust materials significantly contributes to particulate matter (PM), nitrogen oxides (NOx), and carbon dioxide (CO2) emissions (European Environment Agency, 2016) . These emissions disperse into the ambient air as trafficrelated air pollution (TRAP), which degrades ambient air quality.  \nHumans exposed to TRAP are at a higher risk of developing a wide range of adverse health effects, from premature mortality and cardiovascular illness to cognitive and metabolic effects (Fu et al., 2021; Khreis, 2020) . On top of these direct effects, there are additional societal burdens: medical costs, missed school days and workdays (among others Nurmagambetov et al. 2018), reduced workers’ productivity (Chang et al. 2016), and brain drain (Xue et al. 2021) .  \nMost human exposures to TRAP happen in urban areas (Kura et al., 2013) . Even if air quality in western countries has improved enormously in the past decades, most cities around the world struggle to meet air quality standards and guidelines (World Health Organization) . Since the number of urban residents is expected to grow rapidly around the world (United Nations and Department of Economic and Social Affairs Population Division, 2022), a greater quantity of people will soon be at risk of TRAP exposure.  \nWith this in mind, the aim of this work is to analyse policy actions and institutional changes in local governance structures as determinants of air pollutant reductions in US urban areas over the last decade.  \nWe exploit the designation of Transport Management Areas (TMAs) as a quasi-experimental framework. TMAs are designated by the US Secretary of Transportation for urbanized areas that overcome the population threshold of 200,000 as defined by the Bureau of Census, in recognition of the complexity of transportation issues. They are subject to several transportation planning requirements among which a Congestion Management Process and an Air Quality Plan.  \nFrom the methodological point of view, we rely on Regression Discontinuity Design (RDD) techniques, a long-standing way to obtain credible causal estimates that is gaining increasing popularity in recent times (among others, Cattaneo and Titiunik, 2022) . Like other causal inference approaches, the RDD can benefit from the combination with machine learning methods, both to carry out supplementary analyses enhancing the credibility of the results and to handle specific com","cbCaiafrWwEbGlRj","https://ap.wps.com/l/cbCaiafrWwEbGlRj","pdf",208497,1,6,"English","en",105,"# Introduction\n## Transport Management Areas as quasi-experimental framework\n## Regression Discontinuity Design (RDD)\n# Methods\n## Regression Discontinuity Design (causal inference overview)","[{\"question\":\"What role do machine learning algorithms play in the data and analysis?\",\"answer\":\"Machine learning is used to generate raw inputs from satellite imagery and monitoring-station records, and it can support supplementary analyses such as bandwidth determination within the RDD setting.\"}]","Evaluation of pollution containment policies in the US and the role of machine learning algorithms | PDF",1785935916,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"evaluation-of-pollution-containment-policies-in-the-us-and-the-role-of-machine-learning-algorithms","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-pollution-containment-policies-in-the-us-and-the-role-of-machine-learning-algorithms/126961/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What role do machine learning algorithms play in the data and analysis?","Question",{"text":76,"@type":77},"Machine learning is used to generate raw inputs from satellite imagery and monitoring-station records, and it can support supplementary analyses such as bandwidth determination within the RDD setting.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":99,"slug":130},19,"General","general"]