Spatial-temporal variations of summertime ozone concentrations across a metropolitan area using a network of low-cost monitors to develop 24 hourly land-use regression models.

ABSTRACT: Ten relatively-low-cost ozone monitors (Aeroqual Series 500 with OZL ozone sensor) were deployed to assess the spatial and temporal variability of ambient ozone concentrations across residential areas in the Monroe County, New York from June to October 2017. The monitors were calibrated in the laboratory and then deployed to a local air quality monitoring site where they were compared to the federal equivalent method values. These correlations were used to correct the measured ozone concentrations. The values were also used to develop hourly land use regression models (LUR) based on the deletion/substitution/addition (D/S/A) algorithm that can be used to predict the spatial and temporal concentrations of ozone at any hour of a summertime day and given location in Monroe County. Adjusted R2 values were high (average 0.83) with the highest adjusted R2 for the model between 8 and 9?AM (i.e. 1-2?h after the peak of primary emissions during the morning rush hours). Spatial predictors with the highest positive effects on ozone estimates were high intensity developed areas, low and medium intensity developed areas, forests?+?shrubs, average elevation, Interstate?+?highways, and the annual average vehicular daily traffic counts. These predictors are associated with potential emissions of anthropogenic and biogenic precursors. Maps developed from the models exhibited reasonable spatial and temporal patterns, with low ozone concentrations overnight and the highest concentrations between 11?AM and 5?PM. The adjusted R2 between the model predictions and the measured values varied between 0.79 and 0.87 (mean?=?0.83). The combined use of the network of low-cost monitors and LUR modeling provide useful estimates of intraurban ozone variability and exposure estimates that will be used in future epidemiological studies.

SUBMITTER: Masiol M 

PROVIDER: S-EPMC6407642 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature