Paired Indoor and Outdoor Nitrogen Dioxide Associated With Childhood Asthma Outcomes in a Mixed Rural-Urban Setting: A Feasibility Study

INTRODUCTION

Nitrogen dioxide (NO2) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO2 levels in childhood asthma in a mixed rural-urban setting of North America.

METHODS

This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO2 levels in urban and rural residential areas and the association with asthma control status collected from participants' asthma diaries during the study period.

RESULTS

Outdoor NO2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO2 was significantly associated with urban residential living area (P = .014) and lower socioeconomic status (SES) (P = .027). For both seasons, indoor NO2 was significantly higher (P < .05) in rural versus urban areas and in homes with gas versus electric stoves (P < .05). Asthma control status was not associated with level of indoor or outdoor NO2 in this cohort.

CONCLUSIONS

NO2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO2 concentration with health effect on asthma in mixed rural-urban settings.

Land-Use Regression Modeling to Estimate NO2 and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO2), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO2, benzene, toluene, and m-p-xylene were 28.4 µg/m3, 2.40 µg/m3, 15.36 µg/m3, and 0.21 µg/m3, respectively. In terms of model-based R2 values, the model for NO2 included four independent variables, showing R2 = 0.65. While the benzene and m-p-xylene models showed the same R2 values (0.43), toluene showed a lower R2 value (0.35). We estimated long-term concentrations of NO2 and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

Spatial analysis of volatile organic compounds using passive samplers in the Rubbertown industrial area of Louisville, Kentucky, USA

Select volatile organic compounds (VOCs) were measured in the vicinity of chemical facilities and other operations in the Rubbertown industrial area of Louisville, Kentucky (USA) using modified EPA Methods 325A/B passive sampler tubes. Two-week, time-integrated passive samplers were deployed at ten sites which were aggregated into three site groups of varying distances from the Rubbertown area facilities. In comparison to canister data from 2001 to 2005, two of the sites suggested generally lower current VOC levels. Good precision was obtained from the duplicate tubes (≤ 12%) for benzene, toluene, ethylbenzene, and xylene isomers (BTEX), styrene, 1,3-butadiene, perchloroethylene, and other trace VOCs. BTEX, styrene, and 1,3-butadiene concentrations were statistically significantly higher at two site groups near Rubbertown sources than the site group farther away. As found in a similar study in South Philadelphia, BTEX concentrations were also lower for sites farther from a source, though the decline was less pronounced on a percentage basis in Rubbertown. These results suggest that EPA Methods 325A/B can be useful to assess VOC gradients for emissions from chemical facilities besides fenceline benzene levels from refineries.

Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data

Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R²: 0.56; Root Mean Square Error [RMSE]: 0.32 μg/m³) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data.

Land Use Regression Models for Alkylbenzenes in a Middle Eastern Megacity: Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR)

Land use regression (LUR) has not been applied thus far to ambient alkylbenzenes in highly polluted megacities. We advanced LUR models for benzene, toluene, ethylbenzene, p-xylene, m-xylene, o-xylene (BTEX), and total BTEX using measurement based estimates of annual means at 179 sites in Tehran megacity, Iran. Overall, 520 predictors were evaluated, such as The Weather Research and Forecasting Model meteorology predictions, emission inventory, and several new others. The final models with R² values ranging from 0.64 for p-xylene to 0.70 for benzene were mainly driven by traffic-related variables but the proximity to sewage treatment plants was present in all models indicating a major local source of alkylbenzenes not used in any previous study. We further found that large buffers are needed to explain annual mean concentrations of alkylbenzenes in complex situations of a megacity. About 83% of Tehran's surface had benzene concentrations above air quality standard of 5 μg/m³ set by European Union and Iranian Government. Toluene was the predominant alkylbenzene, and the most polluted area was the city center. Our analyses on differences between wealthier and poorer areas also showed somewhat higher concentrations for the latter. This is the largest LUR study to predict all BTEX species in a megacity.

Spatial analysis of volatile organic compounds in South Philadelphia using passive samplers

Select volatile organic compounds (VOCs) were measured in the vicinity of a petroleum refinery and related operations in South Philadelphia, Pennsylvania, USA, using passive air sampling and laboratory analysis methods. Two-week, time-integrated samplers were deployed at 17 sites, which were aggregated into five site groups of varying distances from the refinery. Benzene, toluene, ethylbenzene, and xylene isomers (BTEX) and styrene concentrations were higher near the refinery's fenceline than for groups at the refinery's south edge, mid-distance, and farther removed locations. The near fenceline group was significantly higher than the refinery's north edge group for benzene and toluene but not for ethylbenzene or xylene isomers; styrene was lower at the near fenceline group versus the north edge group. For BTEX and styrene, the magnitude of estimated differences generally increased when proceeding through groups ever farther away from the petroleum refining. Perchloroethylene results were not suggestive of an influence from refining. These results suggest that emissions from the refinery complex contribute to higher concentrations of BTEX species and styrene in the vicinity of the plant, with this influence declining as distance from the petroleum refining increases.

IMPLICATIONS

Passive sampling methodology for VOCs as discussed here is employed in recently enacted U.S. Environmental Protection Agency Methods 325A/B for determination of benzene concentrations at refinery fenceline locations. Spatial gradients of VOC concentration near the refinery fenceline were discerned in an area containing traffic and other VOC-related sources. Though limited, these findings can be useful in application of the method at such facilities to ascertain source influence.

Volatile organic compounds at two oil and natural gas production well pads in Colorado and Texas using passive samplers

A pilot study was conducted in application of the U.S. Environmental Protection Agency (EPA) Methods 325A/B variant for monitoring volatile organic compounds (VOCs) near two oil and natural gas (ONG) production well pads in the Texas Barnett Shale formation and Colorado Denver-Julesburg Basin (DJB), along with a traffic-dominated site in downtown Denver, CO. As indicated in the EPA method, VOC concentrations were measured for 14-day sampling periods using passive-diffusive tube samplers with Carbopack X sorbent at fenceline perimeter and other locations. VOCs were significantly higher at the DJB well pad versus the Barnett well pad and were likely due to higher production levels at the DJB well pad during the study. Benzene and toluene were significantly higher at the DJB well pad versus downtown Denver. Except for perchloroethylene, VOCs measured at passive sampler locations (PSs) along the perimeter of the Barnett well pad were significantly higher than PSs farther away. At the DJB well pad, most VOC concentrations, except perchloroethylene, were significantly higher prior to operational changes than after these changes were made. Though limited, the results suggest passive samplers are precise (duplicate precision usually ≤10%) and that they can be useful to assess spatial gradients and operational conditions at well pad locations over time-integrated periods.

IMPLICATIONS

Recently enacted EPA Methods 325A/B use passive-diffusive tube samplers to measure benzene at multiple fenceline locations at petrochemical refineries. This pilot study presents initial data demonstrating the utility of Methods 325A/B for monitoring at ONG facilities. Measurements revealed elevated concentrations reflective of production levels and spatial gradients of VOCs relative to source proximity at the Barnett well pad, as well as operational changes at the DJB well pad. Though limited, these findings indicate that Methods 325A/B can be useful in application to characterize VOCs at well pad boundaries.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance.

IMPLICATIONS

The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

Evaluation of land use regression models for nitrogen dioxide and benzene in Four US cities

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO₂) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO(2) across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006-2007 (2006-7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006-7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO(2)) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO(2) concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between 1999 and 2006-7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration.