Understanding how local land use and land cover (LULC) shapes intra-urban concentrations of atmospheric pollutants—and thus human health—is a key component in designing healthier cities. Here, NO2 is modeled based on spatially dense summer and winter NO2 observations in Portland-Hillsboro-Vancouver (USA),
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Understanding how local land use and land cover (LULC) shapes intra-urban concentrations of atmospheric pollutants—and thus human health—is a key component in designing healthier cities. Here, NO
2 is modeled based on spatially dense summer and winter NO
2 observations in Portland-Hillsboro-Vancouver (USA), and the spatial variation of NO
2 with LULC investigated using random forest, an ensemble data learning technique. The NO
2 random forest model, together with BenMAP, is further used to develop a better understanding of the relationship among LULC, ambient NO
2 and respiratory health. The impact of land use modifications on ambient NO
2, and consequently on respiratory health, is also investigated using a sensitivity analysis. We find that NO
2 associated with roadways and tree-canopied areas may be affecting annual incidence rates of asthma exacerbation in 4–12 year olds by +3000 per 100,000 and −1400 per 100,000, respectively. Our model shows that increasing local tree canopy by 5% may reduce local incidences rates of asthma exacerbation by 6%, indicating that targeted local tree-planting efforts may have a substantial impact on reducing city-wide incidence of respiratory distress. Our findings demonstrate the utility of random forest modeling in evaluating LULC modifications for enhanced respiratory health.
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