Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Patton, Allison P.; Milando, Chad W.; Durant, John L.; Kumar, Prashant

doi:10.1021/acs.est.6b04633

Cited by 16 publications

(3 citation statements)

References 56 publications

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“…An increasing number of studies use mobile monitoring to characterize specific traffic corridors, − develop and assess empirical models, − and apply those models for epidemiological studies . Use of mobile monitoring may be particularly useful for pollutants with high spatial and temporal variability .…”

Section: Introductionmentioning

confidence: 99%

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Hankey

Sforza

Pierson

2019

Environ. Sci. Technol.

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Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities  an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R 2 (10-fold CV R 2) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R 2 for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.

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Section: Introductionmentioning

confidence: 99%

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Hankey

Sforza

Pierson

2019

Environ. Sci. Technol.

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“…A few recent studies have evaluated the RLINE model output for various pollutant concentrations. However, these studies have either just visually compared observed concentrations versus the RLINE model output (Saha, Khlystov, Snyder, & Grieshop, ) or compared the correlation between the RLINE model output against various forms of observed concentrations: across regional air quality monitors (Milando & Batterman, ; Yu et al, ) or smoothed averages based on MAPL measurements (Patton, Milando, Durant, & Kumar, ). They do not attempt to quantify the potential biases in the RLINE model output.…”

Section: Introductionmentioning

confidence: 99%

Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near‐road environment

2019

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Concentrations of near‐road air pollutants (NRAPs) have increased to very high levels in many urban centers around the world, particularly in developing countries. The adverse health effects of exposure to NRAPs are greater when the exposure occurs in the near‐road environment as compared to background levels of pollutant concentration. Therefore, there is increasing interest in monitoring pollutant concentrations in the near‐road environment. However, due to various practical limitations, monitoring pollutant concentrations near roadways and traffic sources is generally rather difficult and expensive. As an alternative, various deterministic computer models that provide predictions of pollutant concentrations in the near‐road environment, such as the research line‐source dispersion model (RLINE), have been developed. A common feature of these models is that their outputs typically display systematic biases and need to be calibrated in space and time using observed pollutant data. In this paper, we present a nonstationary Bayesian data fusion model that uses a novel data set on monitored pollutant concentrations (nitrogen oxides or NOx and fine particulate matter or PM2.5) in the near‐road environment and, combining it with the RLINE model output, provides predictions at unsampled locations. The model can also be used to evaluate whether including the RLINE model output leads to improved pollutant concentration predictions and whether the RLINE model output captures the spatial dependence structure of NRAP concentrations in the near‐road environment. A defining characteristic of the proposed model is that we model the nonstationarity in the pollutant concentrations by using a recently developed approach that includes covariates, postulated to be the driving force behind the nonstationary behavior, in the covariance function.

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“…In view of the differences in population, traffic flow and human activities between the functional areas, the mass of build-up particles on the road, the metal concentration and the emission factors of road dust were different (Liu et al, 2019;Patton et al, 2017).…”

Section: -3 Figs S3mentioning

confidence: 99%

Seasonal source identification and source-specific health risk assessment of pollutants in road dust

Wang

Huang‬‬‬‬

Mulligan

2021

Environ Sci Pollut Res

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Human exposure to metals on road dust might have potential health risks through touching, ingesting, and inhaling. There were limited studies to link seasonal emission sources to health risks from metals on road dust. In this study, metals on road dust from different functional areas were seasonally monitored. The pollutant sources in study city varied slightly with the seasons, but the major pollutant source in the particular study site were significantly affected by the seasons. By combining the source apportionment model (PMF), line sources model and health risk models (HI: Hazard index and ILCR: Incremental Lifetime Carcinogenic risk), industrial and construction activity was identified as the crucial source of both the pollutants on road dust (29% -47%), and the HI for adults (27% -45%) and children (41% -50%) in different seasons.The traffic non-exhaust emission dominated in the carcinogenic risks for children in spring (45%) and summer (36%). Factors such as seasons, particle size, metal bioavailability, human exposure time, and exposure area were all taken into consideration to avoid overestimating or underestimating health risks. The carcinogenic risks for children (1.6 E-06) and adults (2.8 E-06) exposed to Cr both exceed the minimum threshold (10 -6 ). Measured metals mainly posed hazard to human health through ingestion route. Pb and Mn, Fe and Mn were the main harmful elements that induced non-carcinogenic risks for adults and children, respectively. Effectively identifying the source-specific health risks in different seasons will help in the formulation of adaptive strategies to diminish the potential risks.

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Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Cited by 16 publications

References 56 publications

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near‐road environment

Seasonal source identification and source-specific health risk assessment of pollutants in road dust

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