{"id":891531,"date":"2022-10-24T09:48:22","date_gmt":"2022-10-24T16:48:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2024-01-22T12:09:35","modified_gmt":"2024-01-22T20:09:35","slug":"intraurban-no2-hotspot-detection-via-clustering-of-in-situ-remote-and-modeled-air-quality-data-products","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/intraurban-no2-hotspot-detection-via-clustering-of-in-situ-remote-and-modeled-air-quality-data-products\/","title":{"rendered":"Intraurban NO2 hotspot detection via clustering of in-situ, remote, and modeled air quality data products"},"content":{"rendered":"

Novel\u00a0<\/span>air quality\u00a0<\/span>data sources promise unprecedented insights on intra-urban variations in\u00a0<\/span>air pollution<\/span>\u00a0<\/span>by enabling\u00a0<\/span>stakeholders\u00a0<\/span>to\u00a0<\/span>identify<\/span>\u00a0and mitigate hotspots<\/span>.<\/span>\u00a0However, s<\/span>parse regulatory networks limit validation<\/span>\u00a0of novel datasets<\/span>,<\/span>\u00a0resulting in\u00a0<\/span>pollutant exposure<\/span>\u00a0estimates\u00a0<\/span>that are<\/span>\u00a0likely to be noisy\u00a0<\/span>and difficult to cross-analyze<\/span>\u00a0across platforms<\/span>.<\/span>\u00a0<\/span>In this study,\u00a0<\/span>we\u00a0<\/span>identify<\/span>\u00a0and evaluate\u00a0<\/span>clusters<\/span>\u00a0of\u00a0<\/span>NO<\/span><\/span>2<\/span><\/span><\/sub>\u00a0<\/span>using the\u00a0<\/span>Getis<\/span>-Ord G* statistic\u00a0<\/span>across Chicago, IL using three\u00a0<\/span>novel<\/span>\u00a0<\/span>air quality datasets<\/span>:<\/span>\u00a0(1) a two-way\u00a0<\/span>coupled\u00a0<\/span>WRF-CMAQ simulation<\/span>\u00a0performed at\u00a0<\/span>1.3 km\u00a0<\/span>resolution<\/span>; (2) the\u00a0<\/span>TropOMI<\/span>\u00a0satellite instrument; and (3) a<\/span>\u00a0high-density<\/span>\u00a0network of<\/span>\u00a0l<\/span>ow-cost\u00a0<\/span>air quality\u00a0<\/span>sensors deployed through the Microsoft Eclipse project. We\u00a0<\/span>identify<\/span>\u00a0a large, statistically significant cluster of heightened exposures that is\u00a0<\/span>observed<\/span>\u00a0across all three data sources, enabling us to report with high confidence the presence of a \u201ctrue\u201d hotspot<\/span>,<\/span>\u00a0despite a dearth of regulatory data in the affected area. Moreover, using the temporally fine-grained data sets (WRF-CMAQ and Eclipse), we\u00a0<\/span>observe<\/span>\u00a0that the hotspot is consistent across dominant wind directions. By analyzing the disagreement across\u00a0<\/span>clusters,<\/span>\u00a0we may systematically analyze the reasons for divergence.<\/span>\u00a0For example,<\/span>\u00a0a hotspot that\u00a0<\/span>emerges<\/span>\u00a0in the<\/span>\u00a0observational datasets<\/span>\u00a0but\u00a0<\/span>not<\/span>\u00a0modeled dataset\u00a0<\/span>enables us to interrogate model biases with respect to\u00a0<\/span>underlying\u00a0<\/span>emissions\u00a0<\/span>and meteorological performance<\/span>.\u00a0<\/span>To contrast,\u00a0<\/span>hotspot<\/span>s<\/span>\u00a0<\/span>simulated<\/span>\u00a0<\/span>by WRF-CMAQ\u00a0<\/span>but not\u00a0<\/span>observed<\/span>\u00a0by sensors<\/span>\u00a0<\/span>enable<\/span>\u00a0us to prioritize new locations for sensor deployment. This work offers an example of how researchers can\u00a0<\/span>utilize<\/span>\u00a0and build confidence in<\/span>\u00a0multiple sources of novel air quality data<\/span>. As such, these\u00a0<\/span>complementary<\/span>\u00a0tools\u00a0<\/span>can be used<\/span>\u00a0to<\/span>\u00a0both<\/span>\u00a0evaluate confidence in policy-relevant insights and<\/span>\u00a0to<\/span>\u00a0interrogate and improve\u00a0<\/span>discrepancies across datasets<\/span>.<\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Novel\u00a0air quality\u00a0data sources promise unprecedented insights on intra-urban variations in\u00a0air pollution\u00a0by enabling\u00a0stakeholders\u00a0to\u00a0identify\u00a0and mitigate hotspots.\u00a0However, sparse regulatory networks limit validation\u00a0of novel datasets,\u00a0resulting in\u00a0pollutant exposure\u00a0estimates\u00a0that are\u00a0likely to be noisy\u00a0and difficult to cross-analyze\u00a0across platforms.\u00a0In this study,\u00a0we\u00a0identify\u00a0and evaluate\u00a0clusters\u00a0of\u00a0NO2\u00a0using the\u00a0Getis-Ord G* statistic\u00a0across Chicago, IL using three\u00a0novel\u00a0air quality datasets:\u00a0(1) a two-way\u00a0coupled\u00a0WRF-CMAQ simulation\u00a0performed at\u00a01.3 km\u00a0resolution; (2) the\u00a0TropOMI\u00a0satellite instrument; and (3) a\u00a0high-density\u00a0network of\u00a0low-cost\u00a0air 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