Intraurban NO2 hotspot detection across multiple air quality products
High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when diffe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/acf7d5 |
| _version_ | 1797679236386914304 |
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| author | Anastasia Montgomery Madeleine I G Daepp Marah I Abdin Pallavi Choudhury Sara Malvar Scott Counts Daniel E Horton |
| author_facet | Anastasia Montgomery Madeleine I G Daepp Marah I Abdin Pallavi Choudhury Sara Malvar Scott Counts Daniel E Horton |
| author_sort | Anastasia Montgomery |
| collection | DOAJ |
| description | High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO _2 hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-Ord G _i ^* statistic to identify hotspots of NO _2 and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights. |
| first_indexed | 2024-03-11T23:11:35Z |
| format | Article |
| id | doaj.art-806457da32f048869cd7e89d864a567e |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-11T23:11:35Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-806457da32f048869cd7e89d864a567e2023-09-21T07:39:04ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-01181010401010.1088/1748-9326/acf7d5Intraurban NO2 hotspot detection across multiple air quality productsAnastasia Montgomery0https://orcid.org/0000-0001-7742-9102Madeleine I G Daepp1Marah I Abdin2Pallavi Choudhury3Sara Malvar4Scott Counts5Daniel E Horton6https://orcid.org/0000-0002-2065-4517Department of Earth and Planetary Sciences, Northwestern University , Evanston, IL, United States of AmericaMicrosoft Research , Redmond, WA, United States of AmericaMicrosoft Research , Redmond, WA, United States of AmericaMicrosoft Research , Redmond, WA, United States of AmericaMicrosoft Research , Redmond, WA, United States of AmericaMicrosoft Research , Redmond, WA, United States of AmericaDepartment of Earth and Planetary Sciences, Northwestern University , Evanston, IL, United States of America; Trienens Institute for Sustainability and the Environment, Northwestern University , Evanston, IL, United States of AmericaHigh-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO _2 hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-Ord G _i ^* statistic to identify hotspots of NO _2 and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.https://doi.org/10.1088/1748-9326/acf7d5air pollutionmodelingmonitoringnitrogen dioxidehotspot |
| spellingShingle | Anastasia Montgomery Madeleine I G Daepp Marah I Abdin Pallavi Choudhury Sara Malvar Scott Counts Daniel E Horton Intraurban NO2 hotspot detection across multiple air quality products Environmental Research Letters air pollution modeling monitoring nitrogen dioxide hotspot |
| title | Intraurban NO2 hotspot detection across multiple air quality products |
| title_full | Intraurban NO2 hotspot detection across multiple air quality products |
| title_fullStr | Intraurban NO2 hotspot detection across multiple air quality products |
| title_full_unstemmed | Intraurban NO2 hotspot detection across multiple air quality products |
| title_short | Intraurban NO2 hotspot detection across multiple air quality products |
| title_sort | intraurban no2 hotspot detection across multiple air quality products |
| topic | air pollution modeling monitoring nitrogen dioxide hotspot |
| url | https://doi.org/10.1088/1748-9326/acf7d5 |
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