Spatiotemporal Land Use Regression Model Research Articles

The association of in-utero exposure to air pollution and atherogenic index of plasma in newborns

BackgroundPrenatal exposure to particulate matter (PM) and traffic was associated with the programming of cardiovascular diseases (CVDs) in early life. However, the exact underlying mechanisms are not fully understood. Therefore, we aimed to evaluate the association between in-utero exposure to PMs and traffic indicators with the atherogenic index of plasma (AIP) in newborns, which is a precise index reflecting an enhancement of lipid risk factors for CVDs.MethodsIn this cross-sectional study, a total of 300 mother-newborn pairs were enrolled in Sabzevar, Iran. Spatiotemporal land-use regression models were used to estimate the level of PM1, PM2.5 and PM10 at the mother's residential address. The total length of streets in different buffers (100,300 and 500m) and proximity to major roads were calculated as indicators of traffic. The AIP of cord blood samples was calculated using an AIP calculator. Multiple linear regression models were used to examine the association of PM concentrations as well as traffic indicators with AIP controlled for relevant covariates.ResultsPM2.5 exposure was significantly associated with higher levels of AIP in newborns. Each interquartile range (IQR) increment of PM2.5 concentration at the mothers' residential addresses was associated with a 5.3% (95% confidence interval (CI): 0.0, 10.6%, P = 0.04) increase in the AIP. Associations between PM1, PM10 and traffic indicators with cord blood level of AIP were positive but not statistically significant.ConclusionOur findings showed that in utero exposure to PM2.5 may be associated with CVDs programming through the increase of atherogenic lipids.

Spatio-temporal modeling of airborne pollen concentrations

Keywords: Pollen, Machine Learning, Allergic Disease Abstract Background and aim High concentrations of airborne pollen trigger seasonal allergies and possibly severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies to determine the effects on cardiovascular, respiratory, and cognitive health. We aimed to develop a spatiotemporal land-use regression model for pollen, predicting daily concentrations at a fine spatial resolution of 1x1km across Switzerland between 2003 and 2020. Methods Daily pollen concentrations for hazel, alder, ash, birch, and grasses were available from 14 sites. We considered a range of spatial (elevation, tree type), temporal (date, season, month, week and day of the year, national daily pollen concentration) and spatiotemporal predictors (wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index (NDVI), and land-use (CLC, Landsat satellite) to explain variation in total pollen concentration for five specific pollen species. We applied a range of feature engineering techniques to encode categorical variables (land-use) and fill in missing values (Landsat). We applied a random forest model with 5-fold cross-validation. Results The median grass pollen concentration was 24 pollen/m3 (P5-P95 range 0-187 pollen/m3) during the main grass pollen season (May-July for all years). Preliminary results of a model predicting grass pollen concentration achieved an overall R2 of 0.74 and a root mean squared error (RMSE) of 24.12 pollen/m3 (cross-validation). Temperature, humidity, wind speed, NDVI, Landsat, average national daily pollen concentration and date features were the most important predictors for grass pollen concentration. Conclusions Building upon national observed pollen concentrations and using random forest machine learning, these spatiotemporal pollen models will serve to estimate individual residential pollen exposure. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions using historical data from the Swiss National Cohort and the Swiss Federal Office of Statistics.

Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK.

Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO2), ozone (O3) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO2, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R2 increased to 0.76 from 0.71 for NO2, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O3. The CV-R2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R2 = 0.80 for NO2, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O3). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.

A framework for integrated energy and exposure to ambient pollution (iEnEx) assessment toward low-carbon, healthy, and equitable cities

• A framework to explore urban energy and exposure disparities in cities introduced. • Hourly spatiotemporal LUR models with gradient boosting machine developed. • Local environmental data captured from Chicago's smart cities platforms. • Correlation between spatial disparities of energy burden and exposure to PM2.5 captured. • Households with higher energy burden have less opportunity to use natural ventilation. This research develops a framework for integrated energy and exposure to ambient pollution (iEnEx) assessment addressing energy, health, and equity as an interconnected problem within the built environment. Its focus is to explore spatial disparities of households’ energy burden and their exposure to ambient particulate matters (PM 2.5 as end point) in supporting energy efficiency goals of cities. In this study, energy burden is defined as energy affordability of households for paying energy costs. And natural ventilation (NV) is examined as a catalyst of building energy efficiency to measure tradeoffs between energy burden mitigation and exposure to ambient PM 2.5 pollution. The framework is built upon a five-step workflow using spatiotemporal land use regression (LUR) with gradient boosting machine (GBM) and bringing human behavioral patterns along with array of urban big data from smart cities platforms into the model to improve the explanatory capacities of the exposure model. We tested Chicago, IL as a case study. Findings indicate the effectiveness of the proposed framework in integrating urban energy and human health systems for envisioning urban building energy reduction goals. A web-based interactive platform is designed to communicate the results. iEnEx framework can assist designers, engineers, planners, and policymakers in better understanding these systems from environmental and social lenses in the context of equity within the built environment to ensure future sustainable cities.

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R2 compared with cross-validation results. For MLR models, inflation of both R2 and R2CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM2.5 levels. Inflated within-sample R2 also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM2.5 concentrations.

The ozone climate penalty, NAAQS attainment, and health equity along the Colorado Front Range

BackgroundWhile ozone levels in the USA have decreased since the 1980s, the Denver Metro North Front Range (DMNFR) region remains in nonattainment of the National Ambient Air Quality Standard (NAAQS).ObjectiveTo estimate the warm season ozone climate penalty to characterize its impact on Colorado Front Range NAAQS attainment and health equity.MethodsMay to October ozone concentrations were estimated using spatio-temporal land-use regression models accounting for climate and weather patterns. The ozone climate penalty was defined as the difference between the 2010s concentrations and concentrations predicted using daily 2010s weather adjusted to match the 1950s climate, holding constant other factors affecting ozone formation.ResultsThe ozone climate penalty was 0.5–1.0 ppb for 8-h max ozone concentrations. The highest penalty was around major urban centers and later in the summer. The penalty was positively associated with census tract-level percentage of Hispanic/Latino residents, children living within 100–200% of the federal poverty level, and residents with asthma, diabetes, fair or poor health status, or lacking health insurance.SignificanceThe penalty increased the DMNFR ozone NAAQS design values, delaying extrapolated future attainment of the 2008 and 2015 ozone standards by approximately 2 years each, to 2025 and 2035, respectively.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015–16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

A novel hybrid spatiotemporal land use regression model system at the megacity scale

Air pollution has become a global problem and can cause serious damage to human health. Epidemiological studies on the long-term exposure to air pollution can reveal the extent of this damage. Spatiotemporal land use regression (LUR) models can be used to obtain long-term pollutant concentration surfaces with high spatiotemporal resolution. However, previously established spatiotemporal LUR models generally exhibit poor spatial prediction performances in some time panels compared with their average performances. These inaccurate pollutant concentrations lead to misclassification errors in epidemiological studies. To solve this problem, a hybrid spatiotemporal LUR model system is proposed in this study, which consists of support vector regression (SVR), multiple linear regression (MLR), and a special spatiotemporal (ST) algorithm. Three SVR layers were used for the main prediction, whereas MLR and ST were used to supplement time panels with poor spatial prediction performances. In addition, temporal segmentation modeling was adopted for SVR to further improve the performance. We used the megacity Tianjin in China for our case study and six target air pollutants (CO, NO2, O3, PM10, PM2.5, and SO2). The superiority of our model system was tested by cross-validation. The results show that the number of days on which the R2cv of the model is higher than 0.6 for CO, NO2, O3, PM10, PM2.5, and SO2 is 363, 364, 362, 357, 360, and 362, respectively, whereas the mean of the daily R2cv on these days is 0.911, 0.903, 0.891, 0.879, 0.866, and 0.883, respectively. Based on the use of our model system, a relatively high spatial prediction performance was achieved for almost all time panels. This model system can be applied to cohort health studies to obtain the pollutant concentration surfaces of any time panel with high reliability and reduce the exposure measurement errors of misclassifications.

A spatio-temporal land use regression model to assess street-level exposure to black carbon

Estimation of exposure to air pollution using land use regression (LUR) models often focuses on spatial variation in (annual) average concentration. However, temporal variability is known to be an important factor for exposure. To estimate the short-term street-level exposure to black carbon (BC), we build a spatio-temporal LUR model by including time-dependent variables as predictor variables. We developed and evaluated the model based on data from an opportunistic mobile monitoring campaign in which city employees measured black carbon (BC) during their surveillance tours. Exposure estimates based on the hourly LUR model are more accurate than those based on a fixed site monitoring station or on a spatial LUR model, and can be used to estimate exposure of cyclists or pedestrians to traffic-related pollution based on a GPS track. We demonstrate the potential of building a real-time dynamic pollution map based on unstructured opportunistic measurements to provide personalized exposure information.

Spatiotemporal ozone pollution LUR models: Suitable statistical algorithms and time scales for a megacity scale

Abstract Ambient air ozone (O3), a secondary photochemical pollutant, is seriously harmful to human health. Accurate estimation of O3 exposure requires the ability to monitor O3 surface concentration with a high spatiotemporal resolution. Several spatiotemporal land use regression (LUR) models have integrated meteorological factors based on different statistical algorithms to support such epidemiological studies. From among such various existing statistical algorithms, we aim to identify a high-efficiency modeling method, as well as the most suitable lengths of the modeling period (time scale). Three types of typical spatiotemporal LUR models based on parametric, semi-parametric, and non-parametric statistic methods, respectively, are considered to predict daily ground-level O3 in the megacity of Tianjin, China. Based on monthly, seasonal (cold and warm), and annual time scales, these models include: a series of monthly hybrid LUR (Two-stage) models consisting of two sub-models based on the multiple linear regression (MLR) algorithm, general additive mixed models (GAMMs), and land use random forest (LURF) models. Leave-one-out cross-validation was performed to evaluate the temporal and spatial predictive accuracy of each model using the adjusted coefficient of determination (adjR2CV) and root mean square error (RMSECV). In the GAMMs and LURF models, models using a shorter time scale (monthly models) outperformed those using a longer one. In monthly models, the GAMMs performed the best, with the highest average adjR2CV (0.747) and the lowest average RMSECV (15.721 μg/m3), followed by the LURF models (average adjR2CV = 0.695, average RMSECV = 16.405), and the Two-stage models (average adjR2CV = 0.466, average RMSECV = 23.934). Thus, the modeling format consisting of a shorter time scale and the GAMM algorithm performs relatively well in predicting daily O3 pollution on a megacity scale. These findings can be used to select appropriate modeling methods for epidemiological research of O3 pollution.

Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China

Either long- or short-term of fine particle (PM2.5) exposure is associated with adverse health effects especially for children. Primary school students spend much time in schools whereas PM2.5 prediction for such fine-scale places remains a demanding task, letalone a combined prediction with high temporal resolution. The study aimed to estimate PM2.5 levels of different time scales for primary schools in Jiangsu Province, China. Hourly PM2.5 measurements within the academic year (Sept. 2016-June 2017) were collected from 72 routine monitoring sites. Together with PM2.5 emission inventory and dozens of geographic variables, an advanced spatiotemporal land use regression (LUR) model was employed to estimate PM2.5 concentrations of biweekly, seasonal and academic year levels in Jiangsu Province at 2457 primary school locations. 10-fold cross-validation verified high prediction ability with squared correlations RCV2 of 0.72 for temporal and 0.71 for spatial changes. PM2.5 levels in primary schools in Nanjing and Nantong were >10% higher than that of the corresponding cities while pollution levels in primary schools in Xuzhou were >20% lower. For 10 out of the 13 cities in Jiangsu, PM2.5 levels for primary schools surpassed 70μg/m3 in winter. Schools in Lianyungang, Zhenjiang and Huai'an suffered the most. This study demonstrated the fine-scale prediction ability of the novel spatiotemporal LUR model, as well as the potential and necessity to apply it in epidemiological studies. It also verified the emergency of pollution control for primary schools from cities such as Lianyungang and Zhenjiang.

Gut microbiota partially mediates the effects of fine particulate matter on type 2 diabetes: Evidence from a population-based epidemiological study

BackgroundExperimental studies have indicated that alterations in the gut microbiota might play a role in the pathway of diabetes induction resulting from particulate matter pollution with aerodynamic diameters < 2.5 μm (PM2.5). However, few human studies have examined such experimental findings. Here, we examine the mediating effects of gut microbial dysbiosis on the associations between PM2.5 and particulate matter pollution with aerodynamic diameters < 1 μm (PM1) on diabetes using the Guangdong Gut Microbiome Project (GGMP) dataset. MethodsA multistage cluster sampling method was employed to recruit adult participants from communities in Guangdong. Each participant was interviewed using a questionnaire, fasting blood and stool samples were collected, and the exposure to air pollutants was assessed using a spatiotemporal land-use regression model. The mediation analysis was conducted to estimate the associations among air pollutants, gut microbiota diversity and diabetes. ResultsBoth PM2.5 and PM1 were positively associated with the risks of impaired fasting glucose (IFG) or type 2 diabetes and negatively associated with alpha diversity indices of the gut microbiota. The mediation analyses indicated that the associations of PM2.5 and PM1 with the risk of type 2 diabetes were partially mediated by the decrease in gut microbiota diversity. Moreover, we found that 79 (PM2.5 on IFG), 84 (PM2.5 on type 2 diabetes), 83 (PM1 on IFG) and 89 (PM1 on type 2 diabetes) bacterial taxa could partially mediate the associations of PM2.5 and PM1 with IFG and type 2 diabetes, respectively. The relative abundance of most Firmicutes, Proteobacteria and Verrucomicrobia bacteria were negatively associated with particulate matter (PM) concentrations and the risks of diabetes. ConclusionsLong-term exposure to PM may increase the risk of diabetes, and alterations in the gut microbiota partially explained these associations.

A weekly time-weighted method of outdoor and indoor individual exposure to particulate air pollution

The aim of this study was to estimate the weekly time-weighted (outdoor and indoor activity patterns) individual exposure to particulate air pollutants (PM10, PM2.5 and PM1) of pregnant women. A total of 4928 pregnancy women were recruited during their early pregnancy, and 4278 (86.8%) were successfully followed-up at childbirth. Each individual weekly average PM10 and PM2.5 concentrations at the residential and workplace addresses from three months before pregnancy to childbirth was estimated using a spatiotemporal land use regression (ST-LUR) model, and the weekly PM1 concentration was estimated employing a generalized additive model (GAM) which utilized weekly PM2.5 and meteorological factors as independent predictors. Then, the time-weighted individual exposure to particulate air pollutants during workdays and non-workdays during the period from three months before pregnancy to childbirth was estimated based on the estimated weekly air pollutant concentrations and each participant’s indoor and outdoor activity model, respectively. Data analysis was carried out by R software (version 3.5.1) and packages “SpatioTemporal”, “mgcv” and “splines” were mainly used. This method takes a full consideration of indoor and outdoor activity patterns in the individual exposure to particulate air pollutants.•A ST-LUR model was used to estimate the individual weekly average PM10 and PM2.5 concentrations at their residential and workplace addresses.•A GAM was applied to estimate the weekly average PM1 concentration at individual residential and workplace addresses.•Individual weekly exposure to particulate air pollutants during workdays and non-workdays was assessed based on the estimated particulate air pollutant concentrations and their indoor and outdoor activity model.

A spatiotemporal land-use-regression model to assess individual level long-term exposure to ambient fine particulate matters

We aimed to establish a spatiotemporal land-use-regression (ST-LUR) model assessing individual level long-term exposure to fine particulate matters (PM2.5) among 6627 adults enrolled in Guangdong province, China from 2015 to 2016. We collected weekly average PM2.5 concentration (from the air quality monitoring stations) and visibility, population density, road density and types of land use of each air quality monitoring station and participant’s residential address from April 2013 to December 2016. A ST-LUR model was established using these spatiotemporal data, and was employed to estimate the weekly average PM2.5 concentration of each individual residential address. Data analysis was carried out by R software (version 3.5.1) and the SpatioTemporal package was used. The results showed that the ST-LUR model applying the land use data extracted using a buffer radius of 1300 m had the best modelling fitness. The results of 10-fold cross validation showed that the R2 was 88.86% and the RMSE (Root mean square error) was 5.65 μg/m3. The two-year average of PM2.5 prior to the date of investigation were calculated for each participant. This study provided a novel method to precisely assess individual level long-term exposure to ambient PM2.5, which may extend our understanding on the health impacts of air pollution.•Variables input in the spatiotemporal land-use-regression (ST-LUR) model include visibility, population density, road density, and types of land use.•The land use data should be extracted using a buffer radius of 1300 m.•The R2 of the ST-LUR model was 88.86% and the RMSE was 5.65 μg/m3, indicating the good performance of the model.

Merging Mobile Measurements, Traffic Emissions Models, and Land Use Regression Towards Real-Time Estimation of Traffic-Related Air Pollution

Background: Current Land Use Regression (LUR) modeling approaches lack the spatiotemporal precision needed to match people&amp;#8217;s time-activity patterns for exposure assessment. We developed spatiotemporal LUR models of Particle Number (PN) and Black Carbon (BC) concentrations using two pieces of information: (1) mobile monitoring and (2) on-road emissions estimates from the INTEGRATION microscopic traffic simulation model which tracks vehicle movements, fuel consumption, and emissions every second.Methods: We employed mobile monitoring to collect ~120 hours of PN and BC measurements in Blacksburg, VA. Data collection was stratified by time of day (~10 hours of monitoring for each hour during 7am-7pm). We calibrated the INTEGRATION traffic emissions model to estimate hourly PN and BC emissions (100m spatial scale). We combined these datasets, along with traditional LUR covariates (e.g., land use variables), for model building. We developed and compared three types of models: (1) daytime average models that pool all mobile measurements, (2) hourly models (i.e., single model for each hour of day) and (3) spatiotemporal models (single model incorporating hour of day as a predictor).Results: Model fit for the daytime average models (adj-R2 for PN [BC]: 0.72 [0.66]) were comparable to models developed using fixed-site measurements. The hourly models (i.e., 12 separate models) had modest model fit (mean PN [BC] adj-R2: 0.52 [0.36]). The spatiotemporal models had the poorest model fit among model-types (adj-R2 for PN [BC]: 0.46 [0.27]). Results for the temporally resolved models were comparable to other mobile monitoring campaigns that attempted to model long-term averages suggesting it is possible to develop models with both spatial and temporal precision for real-time dissemination.Conclusions: Our approach to model traffic-related air pollution allows for matching the temporal and spatial resolution of people&amp;#8217;s time-activity patterns towards improved exposure assessment.

National scale spatiotemporal land-use regression model for PM2.5, PM10 and NO2 concentration in China

BackgroundAir pollution epidemiological studies increasingly rely on high-resolution exposure prediction models. However, to date, few models of this type exist for use in China. ObjectivesWe produced a national land-use regression model (LUR) to estimate monthly average PM2.5, PM10 and NO2 from 2014 to 2016 in China. MethodsWe developed a spatiotemporal semi-parametric model using generalized additive mixed models. A variety of predictor variables were included in model: time varying meteorological data, high resolution land cover data from Globaland30, satellite measures of aerosol optical depth, and Geographic Information System (GIS)-derived predictors. We assessed model performance with two cross-validation (CV) approaches, including hold-out CV, and 10-fold CV. ResultsOver 22,000 monthly observations at 1382 monitoring locations were included to estimate the air pollution exposure. The time-varying spatial terms explained 87%, 71%, and 69% of variability with a hold-out cross-validated R2 of 0.85, 0.62, and 0.62 for PM2.5, PM10 and NO2 models, respectively. Models show that meteorological variables, population density, elevation, distance to road, and land cover types were important predictors for air pollution exposure. Conclusionswe have developed a new nationwide model to estimate residence-level air pollution exposures, which can be used in studies of the chronic adverse effects of air pollution.

Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters

The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM2.5, PM10, O3, NO2, CO and SO2 using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM2.5, PM10 and SO2. Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments.

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.

Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

Ambient particulate matter and microRNAs in extracellular vesicles: a pilot study of older individuals.

BackgroundAir pollution from particulate matter (PM) has been linked to cardiovascular morbidity and mortality; however the underlying biological mechanisms remain to be uncovered. Gene regulation by microRNAs (miRNAs) that are transferred between cells by extracellular vesicles (EVs) may play an important role in PM-induced cardiovascular risk. This study sought to determine if ambient PM2.5 levels are associated with expression of EV-encapsulated miRNAs (evmiRNAs), and to investigate the participation of such evmiRNAs in pathways related to cardiovascular disease (CVD).MethodsWe estimated the short- (1-day), intermediate- (1-week and 1-month) and long-term (3-month, 6-month, and 1-year) moving averages of ambient PM2.5 levels at participants’ addresses using a validated hybrid spatio-temporal land-use regression model. We collected 42 serum samples from 22 randomly selected participants in the Normative Aging Study cohort and screened for 800 miRNAs using the NanoString nCounter® platform. Mixed effects regression models, adjusted for potential confounders were used to assess the association between ambient PM2.5 levels and evmiRNAs. All p-values were adjusted for multiple comparisons. In-silico Ingenuity Pathway Analysis (IPA) was performed to identify biological pathways that are regulated by PM-associated evmiRNAs.ResultsWe found a significant association between long-term ambient PM2.5 exposures and levels of multiple evmiRNAs circulating in serum. In the 6-month window, ambient PM2.5 exposures were associated with increased levels of miR-126-3p (0.74 ± 0.21; p = 0.02), miR-19b-3p (0.52 ± 0.15; p = 0.02), miR-93-5p (0.78 ± 0.22; p = 0.02), miR-223-3p (0.74 ± 0.22; p = 0.02), and miR-142-3p (0.81 ± 0.21; p = 0.03). Similarly, in the 1-year window, ambient PM2.5 levels were associated with increased levels of miR-23a-3p (0.83 ± 0.23; p = 0.02), miR-150-5p (0.90 ± 0.24; p = 0.02), miR-15a-5p (0.70 ± 0.21; p = 0.02), miR-191-5p (1.20 ± 0.35; p = 0.02), and let-7a-5p (1.42 ± 0.39; p = 0.02). In silico pathway analysis on PM2.5-associated evmiRNAs identified several key CVD-related pathways including oxidative stress, inflammation, and atherosclerosis.ConclusionsWe found an association between long-term ambient PM2.5 levels and increased levels of evmiRNAs circulating in serum. Further observational studies are warranted to confirm and extend these important findings in larger and more diverse populations, and experimental studies are needed to elucidate the exact roles of evmiRNAs in PM-induced CVD.Electronic supplementary materialThe online version of this article (doi:10.1186/s12989-016-0121-0) contains supplementary material, which is available to authorized users.

Associations between Prenatal Exposure to Black Carbon and Memory Domains in Urban Children: Modification by Sex and Prenatal Stress.

BackgroundWhether fetal neurodevelopment is disrupted by traffic-related air pollution is uncertain. Animal studies suggest that chemical and non-chemical stressors interact to impact neurodevelopment, and that this association is further modified by sex.ObjectivesTo examine associations between prenatal traffic-related black carbon exposure, prenatal stress, and sex with children’s memory and learning.MethodsAnalyses included N = 258 mother-child dyads enrolled in a Boston, Massachusetts pregnancy cohort. Black carbon exposure was estimated using a validated spatiotemporal land-use regression model. Prenatal stress was measured using the Crisis in Family Systems-Revised survey of negative life events. The Wide Range Assessment of Memory and Learning (WRAML2) was administered at age 6 years; outcomes included the General Memory Index and its component indices [Verbal, Visual, and Attention Concentration]. Relationships between black carbon and WRAML2 index scores were examined using multivariable-adjusted linear regression including effect modification by stress and sex.ResultsMothers were primarily minorities (60% Hispanic, 26% Black); 67% had ≤12 years of education. The main effect for black carbon was not significant for any WRAML2 index; however, in stratified analyses, among boys with high exposure to prenatal stress, Attention Concentration Index scores were on average 9.5 points lower for those with high compared to low prenatal black carbon exposure (P 3-way interaction = 0.04).ConclusionThe associations between prenatal exposure to black carbon and stress with children’s memory scores were stronger in boys than in girls. Studies assessing complex interactions may more fully characterize health risks and, in particular, identify vulnerable subgroups.