Surface ozone monitoring and policy: A geospatial decision support tool for suitable location of monitoring stations in urban areas

Northeim, Kari; Tiwari, Chetan; Oppong, Joseph R.

doi:10.1016/j.envsci.2021.09.011

Cited by 1 publication

(3 citation statements)

References 36 publications

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“…A clear example is Wise County, which is the only county in DFW without an ozone air pollution monitor/sensor. In fact, the county has 100% no coverage until the service radius reaches 10 km (coverage less than 5%) and 20 km (coverage less than 25%) (Figures 1-4) [31]. Wise County is in the northwest corner of DFW and most likely has the largest ozone exposures [47,48].…”

Section: Discussionmentioning

confidence: 99%

“…The SVI also contains dichotomized data identifying the 90th-percentile or the most vulnerable populations within each social factor. The decision-making structure for the evaluation of populations covered or not covered by air pollution sensors in DFW from Northeim, Oppong, and Tiwari (2021) is applied to the spatial scales (service radii) of 4, 6, 10, and 20 km in the DFW region [31,32].…”

Section: Methodsmentioning

confidence: 99%

“…Then, to examine the risk measurement of the effect, we perform a multinomial logistic regression for classification and predictive analysis for the 10 and 20 km spatial scales because they most closely represent actual sensor coverage in DFW. As in Northeim, Tiwari, and Oppong (2021), Voronoi catchment areas and proximal allocation heuristic modeling are used to estimate and categorize the regions and populations served by the existing monitoring network [31]. Finally, using the CDC/ATSDR SVI dichotomized data, we examine all 15 variables to identify the 90th percentile and calculate the odds ratio as a measure of association between the exposure and the outcome [33,34].…”

Section: Methodsmentioning

confidence: 99%

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Environmental air pollution remains a major contributor to negative health outcomes and mortality, but the relationship between socially vulnerable populations and air pollution is not well understood. Although air pollution potentially affects everyone, the combination of underlying health, socioeconomic, and demographic factors exacerbate the impact for socially vulnerable population groups, and the United States Clean Air Act (CAA) describes an obligation to protect these populations. This paper seeks to understand how air pollution monitor placement strategies and policy may neglect social vulnerabilities and therefore potentially underestimate exposure burdens in vulnerable populations. Multivariate logistic regression models were used to assess the association between being in an ozone-monitored area or not on 15 vulnerability indicators. It was found that the odds of not being in an ozone-monitored area (not covered, outside) increased for the predictor mobile homes (OR = 4.831, 95% CI [2.500–9.338] and OR = 8.066, 95% CI [4.390–14.820] for the 10 and 20 km spatial units, respectively) and decreased for the predictor multiunit structures (OR = 0.281, 95% CI [0.281–0.548] and OR = 0.130, 95% CI [0.037, 0.457] for the 10 and 20 km spatial units, respectively) and the predictor speaks English “less than well” (OR = 0.521, 95% CI [0.292–0.931] for 10 km). These results indicate that existing pollution sensor coverage may neglect areas with concentrations of highly vulnerable populations in mobile homes, and future monitoring placement policy decisions must work to address this imbalance.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%