Land use regression models for ultrafine particles, fine particles, and black carbon in Southern California

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Characterizing sector-oriented roadside exposure to ultrafine particles (PM0.1) via machine learning models: Implications of covariates influences on sectors variability

Ultrafine particles (UFPs; PM0.1) possess intensified health risk due to their smaller size and unique spatial variability. One of major emission sources for UFPs is vehicle exhaust, which varies based on the traffic composition in each type of roadside sector. The current challenge of epidemiological UFPs study is limited characterization ability due to expensive instruments. This study assessed the UFPs particle number concentrations (UFPs PNC) exposure dose for typical healthy adults and children at three different roadside sectors, including industrial roadside (IN), residential roadside (RS), and urban background (UB). Furthermore, this study also developed and utilized machine learning (ML) algorithms that could accurately characterize the UFPs exposure dose and explain the covariates effects on the model outputs, representing the intra-urban variability of UFPs between sectors. It was found that the average inhaled UFPs dose for healthy adults and children during off-peak season (warm period) were 1.71 ± 0.19 × 1010; 1.28 ± 0.22 × 1010; 1.09 ± 0.18 × 1010 #/hour and 1.33 ± 0.15 × 1010; 0.99 ± 0.17 × 1010; 0.86 ± 0.14 × 1010 #/hour at IN, RS, UB. Inhaled UFPs were mainly deposited in tracheobronchial (TB) respiratory fraction for adults (67.7%) and in alveoli (ALV) fraction for children (67.5%). Among three ML algorithms implemented in this study, XGBoost possessed the highest UFPs PNC exposure dose estimation performances with R2 = 0.965; 0.959; 0.929 & RMSE = 0.79 × 108; 0.54 × 108; 0.15 × 105 #/hour at IN, RS, and UB which then followed by multiple linear regression (MLR), and random forest (RF). Furthermore, SHAP analysis from the XGBoost model has successfully pointed out the spatial variability of each roadside sector by quantifying the approximated contributions of covariates to the model's output. Findings in this study highlighted the potential use of ML models as an alternative for preliminary particle exposure source apportionment.

Association between Airport Ultrafine Particles and Lung Cancer Risk: The Multiethnic Cohort Study

BACKGROUND

Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County.

METHODS

Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted.

RESULTS

Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97-1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00-1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02-1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01-1.49) with a Phet for histology = 0.01.

CONCLUSIONS

This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC.

IMPACT

These results highlight the potential role of airport-related UFP exposure in the development of lung SCC.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Los Angeles Basin's air quality transformation: a long-term investigation on the impacts of PM regulations on the trends of ultrafine particles and co-pollutants

This study investigates the long-term trends of ambient ultrafine particles (UFPs) and associated airborne pollutants in the Los Angeles Basin from 2007 to 2022, focusing on the indirect effects of regulations on UFP levels. The particle number concentration (PNC) of UFPs was compiled from previous studies in the area, and associated co-pollutant data, including nitrogen oxides (NOx), carbon monoxide (CO), elemental carbon (EC), organic carbon (OC), and ozone (O3), were obtained from the chemical speciation network (CSN) database. Over the study period, a general decrease was noted in the PNC of UFPs, NOx, EC, and OC, except for CO, the concentration trends of which did not exhibit a consistent pattern. UFPs, NOx, EC, and OC were positively correlated, while O3 had a negative correlation, especially with NOx. Our analysis discerned two distinct subperiods in pollutant trends: 2007-2015 and 2016-2022. For example, there was an overall decrease in the PNC of UFPs at an annual rate of -850.09 particles/cm3/year. This rate was more pronounced during the first sub-period (2007-2015) at -1814.9 particles/cm3/year and then slowed to -227.21 particles/cm3/year in the second sub-period (2016-2023). The first sub-period (2007-2015) significantly influenced pollutant level changes, exhibiting more pronounced and statistically significant changes than the second sub-period (2016-2022). Since 2016, almost all primary pollutants have stabilized, indicating a reduced impact of current regulations, and emphasizing the need for stricter standards. In addition, the study included an analysis of Vehicle Miles Traveled (VMT) trends from 2007 to 2022 within the Los Angeles Basin. Despite the general increase in VMT, current regulations and cleaner technologies seem to have successfully mitigated the potential increase in increase in PNC. Overall, while a decline in UFPs and co-pollutant levels was observed, the apparent stabilization of these levels underscores the need for more stringent regulatory measures and advanced emission standards.

Outdoor Ultrafine Particulate Matter and Risk of Lung Cancer in Southern California

Rationale: Particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5) is an established cause of lung cancer, but the association with ultrafine particulate matter (UFP; aerodynamic diameter < 0.1 μm) is unclear. Objectives: To investigate the association between UFP and lung cancer overall and by histologic subtype. Methods: The Los Angeles Ultrafines Study includes 45,012 participants aged ⩾50 years in southern California at enrollment (1995-1996) followed through 2017 for incident lung cancer (n = 1,770). We estimated historical residential ambient UFP number concentrations via land use regression and back extrapolation using PM2.5. In Cox proportional hazards models adjusted for smoking and other confounders, we estimated associations between 10-year lagged UFP (per 10,000 particles/cm3 and quartiles) and lung cancer overall and by major histologic subtype (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma). We also evaluated relationships by smoking status, birth cohort, and historical duration at the residence. Measurements and Main Results: UFP was modestly associated with lung cancer risk overall (hazard ratio [HR], 1.03 [95% confidence interval (CI), 0.99-1.08]). For adenocarcinoma, we observed a positive trend among men; risk was increased in the highest exposure quartile versus the lowest (HR, 1.39 [95% CI, 1.05-1.85]; P for trend = 0.01) and was also increased in continuous models (HR per 10,000 particles/cm3, 1.09 [95% CI, 1.00-1.18]), but no increased risk was apparent among women (P for interaction = 0.03). Adenocarcinoma risk was elevated among men born between 1925 and 1930 (HR, 1.13 [95% CI, 1.02-1.26] per 10,000) but not for other birth cohorts, and was suggestive for men with ⩾10 years of residential duration (HR, 1.11 [95% CI, 0.98-1.26]). We found no consistent associations for women or other histologic subtypes. Conclusions: UFP exposure was modestly associated with lung cancer overall, with stronger associations observed for adenocarcinoma of the lung.

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

BACKGROUND

Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

OBJECTIVE

This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto.

METHODS

We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models.

RESULTS

In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses.

CONCLUSION

Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.

Prenatal ambient air pollutants exposure and the risk of stillbirth in Wuhan, central of China

BACKGROUND

The existing studies on the relationships of prenatal ambient air pollutants exposure with stillbirth in the Chinese population are very limited and the results are inconsistent, and the susceptible windows and potential modifiers for air pollutants exposure on stillbirth remain unanswered.

OBJECTIVE

We aimed to determine the relationships between exposure to ambient air pollutants and stillbirth, and explored the susceptible windows and potential modifiers for air pollutants exposure on stillbirth.

METHODS

A population-based cohort was established through the Wuhan Maternal and Child Health Management Information System involving 509,057 mother-infant pairs in Wuhan from January 1, 2011 through September 30, 2017. Personal exposure concentrations of fine particles (PM_2.5), inhalable particles (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) for mothers were estimated based on their residential address during pregnancy using the inverse distance weighted (IDW) method. We used the logistic regression models to determine the associations at different stages of pregnancy with adjustment for confounding factors.

RESULTS

There were 3218 stillbirths and 505,839 live births among the participants. For each 100 μg/m³ of CO and 10 μg/m³ of O₃ increase in the first trimester (conception to 13⁺⁶ weeks), the risk of stillbirth increased by 1.0% (OR = 1.01, 95%CI: 1.00-1.03) and 7.0% (OR = 1.07, 95%CI: 1.05-1.09). In the second trimester (14 weeks-27⁺⁶ weeks), PM_2.5, PM₁₀, CO, and O₃ exposure were closely related to the risk of stillbirth (P＜0.05). In the third trimester (28 weeks to delivery), for each 10 μg/m³ increase in exposure concentrations of PM_2.5, SO₂, and O₃, the risk of stillbirth increased by 3.4%, 5.9%, and 4.0%, respectively. O₃ exposure was positively relevant to the risk of stillbirth (OR = 1.11, 95%CI: 1.08-1.14) in the whole pregnancy. Exposure to NO₂ was not significantly associated with the risk of stillbirth. Stratified analyses also presented a stronger association among mothers with boy infant, living in rural areas, delivering between 2011 and 2013, and those without gestational hypertension and history of stillbirth.

CONCLUSION

This study provides evidence that maternal exposure to PM_2.5, PM₁₀, SO₂, CO, and O₃ were related to the increased risk of stillbirth. Both the second and third trimesters might be vital susceptible windows for stillbirth. Our findings expand the evidence base for the important impacts of air pollution on fetal growth.

Quantitative estimation of the PM2.5 removal capacity and influencing factors of urban green infrastructure

Long-term exposure to PM_2.5 (fine particulate matter with an aerodynamic diameter <2.5 μm) could cause great harm to human health and sustainable development. It remains a challenge to estimate the long-term PM_2.5 removal capacity of nature-based green infrastructure in urban areas. In this paper, the annual PM_2.5 removal capacity of urban green infrastructure (UGI) from 2000 to 2019 in Shenyang was estimated based on the PM2.5 dry deposition model. The spatial heterogeneity of annual PM_2.5 removal capacity were detected Sen-MK test and local spatial autocorrelations analysis. Then the effects of landscape patterns and socioeconomic variables on PM_2.5 removal capacity were explored based on linear regression model. The results illustrated that the PM_2.5 removal capacity of UGI increased significantly from 2000 to 2019 in Shenyang, with the amount of PM_2.5 removal, PM_2.5 removal flux and removal rate increasing by 20.64 Mg/a, 0.0258 g/m²/a, and 0.377 %/a, respectively. The PM_2.5 removal capacity of UGI exhibited spatial heterogeneity in the study area. Specifically, the regions experiencing the increase in PM_2.5 removal capacity of UGI accounted for majority of the old urban area of Shenyang City during the study period; the lower PM_2.5 removal capacity clustered in the center urban area, in which high density impervious surfaces distributed, while the higher PM_2.5 removal capacity mainly gathered in the area with large scale green space; PM_2.5 removal capacity were significantly higher in urban functional zones with a high proportion of green spaces. The landscape metrics representing fragmentation and shape complexity positively affected the annual PM_2.5 removal flux and removal rate, while the aggregation metrics had significantly negative correlations with the PM_2.5 removal flux and removal rate. Moreover, it was also found that population density and GDP negatively affected the PM_2.5 removal capacity of UGI. This study provides a methodological reference and some new insights for future urban landscape planning and air pollution purification.

Ambient ultrafine particle (PM0.1): Sources, characteristics, measurements and exposure implications on human health

The problem of ultrafine particles (UFPs; PM_0.1) has been prevalent since the past decades. In addition to become easily inhaled by human respiratory system due to their ultrafine diameter (<100 nm), ambient UFPs possess various physicochemical properties which make it more toxic. These properties vary based on the emission source profile. The current development of UFPs studies is hindered by the problem of expensive instruments and the inexistence of standardized measurement method. This review provides detailed insights on ambient UFPs sources, physicochemical properties, measurements, and estimation models development. Implications on health impacts due to short-term and long-term exposure of ambient UFPs are also presented alongside the development progress of potentially low-cost UFPs sensors which can be used for future UFPs studies references. Current challenge and future outlook of ambient UFPs research are also discussed in this review. Based on the review results, ambient UFPs may originate from primary and secondary sources which include anthropogenic and natural activities. In addition to that, it is confirmed from various chemical content analysis that UFPs carry heavy metals, PAHs, BCs which are toxic in its nature. Measurement of ambient UFPs may be performed through stationary and mobile methods for environmental profiling and exposure assessment purposes. UFPs PNC estimation model (LUR) developed from measurement data could be deployed to support future epidemiological study of ambient UFPs. Low-cost sensors such as bipolar ion and ionization sensor from common smoke detector device may be further developed as affordable instrument to monitor ambient UFPs. Recent studies indicate that short-term exposure of UFPs can be associated with HRV change and increased cardiopulmonary effects. On the other hand, long-term UFPs exposure have positive association with COPD, CVD, CHF, pre-term birth, asthma, and also acute myocardial infarction cases.

Analysis of spatial distribution characteristics and main influencing factors of heavy metals in road dust of Tianjin based on land use regression models

Land use regression (LUR) models are mainly used for the simulation and prediction of conventional atmospheric pollutants. Whether the LUR models can be expanded to study more toxic and hazardous pollutants (such as heavy metals) remains to be verified. Combined with the factors of road, land use type, population, pollution enterprise, meteorology, and terrain, the LUR models were used to simulate the spatial distribution characteristics of heavy metals in road dust and determine the main influencing factors. Samples of road surface dust were collected from 144 evenly distributed points in Tianjin, China, with 108 modelling points and 36 verification points. The R² values of the LUR models of Cd, Cr, Cu, Ni, and Pb contents were 0.301, 0.412, 0.399, 0.496, and 0.377, and their error rates were 2.72%, 4.96%, 4.64%, 8.91%, and 4.94%, respectively. The error rates of the kriging interpolation models were 3.33%, 6.50%, 5.14%, 18.30%, and 22.87%, which were all greater than those of the LUR models. The estimation effect of the LUR models was more refined than that of the kriging interpolation models. The contents of most heavy metals (except Ni) in road dust of the central area in Tianjin were generally higher than those of the surrounding areas. The heavy metal contents in road dust of Tianjin were mainly affected by road variables and meteorological variables. The LUR models were suitable for small-scale spatial prediction of heavy metals in urban road dust within urban areas.

Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO₂), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO₂. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

Co-kriging with a low-cost sensor network to estimate spatial variation of brake and tire-wear metals and oxidative stress potential in Southern California

Due to regulations and technological advancements reducing tailpipe emissions, an increasing proportion of emissions arise from brake and tire wear particulate matter (PM). PM from these non-tailpipe sources contains heavy metals capable of generating oxidative stress in the lung. Although important, these particles remain understudied because the high cost of actively collecting filter samples. Improvements in electrical engineering, internet connectivity, and an increased public concern over air pollution have led to a proliferation of dense low-cost air sensor networks such as the PurpleAir monitors, which primarily measure unspeciated fine particulate matter (PM2.5). In this study, we model the concentrations of Ba, Zn, black carbon, reactive oxygen species concentration in the epithelial lining fluid, dithiothreitol (DTT) loss, and OH formation. We use a co-kriging approach, incorporating data from the PurpleAir network as a secondary predictor variable and a land-use regression (LUR) as an external drift. For most pollutant species, co-kriging models produced more accurate predictions than an LUR model, which did not incorporate data from the PurpleAir monitors. This finding suggests that low-cost sensors can enhance predictions of pollutants that are costly to measure extensively in the field.

Non-linear models for black carbon exposure modelling using air pollution datasets

Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018-2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R² = 0.828) with a relatively low error (RMSE = 0.48 μg/m³). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R² = 0.633; RMSE = 1.19 μg/m³) due to the lack of N data and PM_2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas.

Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R² and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China

BACKGROUND

Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution.

OBJECTIVE

This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides.

METHOD

A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method.

RESULT

The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm^-3. The 10-fold cross validation R² reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NO_x emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas.

CONCLUSION

We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM₁) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM₁ based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM₁. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m³; PM₁: 56.97 μg/m³) than in fall (BC: 3.74 μg/m³; PM₁: 33.29 μg/m³) and summer (BC: 2.77 μg/m³; PM₁: 27.04 μg/m³). For both BC and PM₁, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R² of the LUR models for BC and PM₁ ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM₁ models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM₁ was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM₁ in typical inner-city environments, and support pollution management.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Evaluation of a commercial database to estimate residence histories in the los angeles ultrafines study

BACKGROUND

Commercial databases can be used to identify participant addresses over time, but their quality and impact on environmental exposure assessment is uncertain.

OBJECTIVE

To evaluate the performance of a commercial database to find residences and estimate environmental exposures for study participants.

METHODS

We searched LexisNexis® for participant addresses in the Los Angeles Ultrafines Study, a prospective cohort of men and women aged 50-71 years. At enrollment (1995-1996) and follow-up (2004-2005), we evaluated attainment (address found for the corresponding time period) and match rates to survey addresses by participant characteristics. We compared geographically-referenced predictors and estimates of ultrafine particulate matter (UFP) exposure from a land use regression model using LexisNexis and survey addresses at enrollment.

RESULTS

LexisNexis identified an address for 69% of participants at enrollment (N = 50,320) and 95% of participants at follow-up (N = 24,432). Attainment rate at enrollment modestly differed (≥5%) by age, smoking status, education, and residential mobility between surveys. The match rate at both survey periods was high (82-86%) and similar across characteristics. When using LexisNexis versus survey addresses, correlations were high for continuous values of UFP exposure and its predictors (rho = 0.86-0.92).

SIGNIFICANCE

Time period and population characteristics influenced the attainment of addresses from a commercial database, but accuracy and subsequent estimation of specific air pollution exposures were high in our older study population.

Putting Fine Particulate Matter and Dementia in the Wider Context of Noncommunicable Disease: Where are We Now and What Should We Do Next: A Systematic Review

INTRODUCTION

A significant proportion of the global population regularly experience air quality poorer than that recommended by the World Health Organization. Air pollution, especially fine particulate matter (PM2.5), is a risk factor for various noncommunicable diseases (NCDs) and is emerging as a risk factor for dementia. To begin to understand the full impact of PM2.5, we review the longitudinal epidemiological evidence linking PM2.5 to both dementia and to other leading NCDs and highlight the evidence gaps. Our objective was to systematically review the current epidemiological evidence for PM2.5 as a risk factor for cognitive decline and incident dementia and to put this in context with a systematic overview of PM2.5 as a potential risk factor in other leading NCDs.

METHODS

We performed 2 systematic reviews. A high-level review of reviews examining the relationship between PM2.5 and leading NCDs and an in-depth review of the longitudinal epidemiological data examining relationships between PM2.5 incident dementia and cognitive decline.

RESULTS

There were robust associations between PM2.5 and NCDs although in some cases the evidence was concentrated on short rather than longer term exposure. For those articles reporting on incident dementia, all reported on longer term exposure and 5 of the 7 eligible articles found PM2.5 to be associated with increased risk.

CONCLUSION

The evidence base for PM2.5 as a risk factor for dementia is growing. It is not yet as strong as that for other NCDs. However, varied measurement/methodology hampers clarity across the field. We propose next steps.

Monitoring the Effective Density of Airborne Nanoparticles in Real Time Using a Microfluidic Nanoparticle Analysis Chip

Determining the effective density of airborne nanoparticles (NPs; particles smaller than 100 nm in diameter) at a point of interest is essential for toxicology and environmental studies, but it currently requires complex analysis systems comprising several high-precision instruments as well as a specially trained operator. To address these limitations, a field-portable and cost-efficient microfluidic NP analysis device is presented, which provides quantitative information on the effective density and size distribution of NPs in real time. Unlike conventional analysis systems, the device can operate in a standalone mode because of the chip operating principle based on the electrostatic/inertial classification and electrical detection methods. Moreover, the device is both compact (16.0 × 10.9 × 8.6 cm³) and light (950 g) owing to the hardware strip down enabled by integrating the essential functions for effective density analysis on a single chip. Quantitative experiments performed to simulate real-life applications utilizing effective density (i.e., effective density-based morphology analysis on engineered NPs and multi-parametric NP monitoring in ambient air) demonstrate that the developed device can be used as an analysis tool in toxicological studies as an on-site sensor for the monitoring of individual NP exposure and environments, for quality monitoring of engineered NPs via aerosol synthesis, and other applications.

Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands

Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm³. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.

Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands

Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm³. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.

Increasing cardiopulmonary effects of ultrafine particles at relatively low fine particle concentrations

Ultrafine particles (UFPs) are of concern because of their high pulmonary deposition efficiency. However, present control measures are generally targeted at fine particles (PM_2.5), with little effect on UFPs. The health effects of UFPs at different PM_2.5 concentrations may provide a basic for controlling UFPs but remain unclear in polluted areas. School children spend the majority of their time in the classrooms. This study investigated the different short-term effects of indoor UFPs on school children in Beijing, China when indoor PM_2.5 concentrations exceeded or satisfied the recently published Chinese standard for indoor PM_2.5. Cardiopulmonary functions of 48 school children, of whom 46 completed, were measured three times. Indoor PM_2.5 and UFPs were monitored in classrooms on weekdays. Measurements were separated into two groups according to the abovementioned standard. Mixed-effect models were used to explore the health effects of the air pollutants. Generally, UFP-associated effects on children's cardiopulmonary function persisted even at relatively low PM_2.5 concentrations, especially on heart rate variability indices. The risks associated with high PM_2.5 concentrations are well-known, but the effects of UFPs on children's cardiopulmonary function deserve more attention even when PM_2.5 has been controlled. UFP control and standard setting should therefore be considered.

Shipping Remains a Globally Significant Source of Anthropogenic PN Emissions Even after 2020 Sulfur Regulation

Shipping is the main source of anthropogenic particle emissions in large areas of the globe, influencing climate, air quality, and human health in open seas and coast lines. Here, we determined, by laboratory and on-board measurements of ship engine exhaust, fuel-specific particle number (PN) emissions for different fuels and desulfurization applied in shipping. The emission factors were compared to ship exhaust plume observations and, furthermore, exploited in the assessment of global PN emissions from shipping, utilizing the STEAM ship emission model. The results indicate that most particles in the fresh ship engine exhaust are in ultrafine particle size range. Shipping PN emissions are localized, especially close to coastal lines, but significant emissions also exist on open seas and oceans. The global annual PN produced by marine shipping was 1.2 × 10²⁸ (±0.34 × 10²⁸) particles in 2016, thus being of the same magnitude with total anthropogenic PN emissions in continental areas. The reduction potential of PN from shipping strongly depends on the adopted technology mix, and except wide adoption of natural gas or scrubbers, no significant decrease in global PN is expected if heavy fuel oil is mainly replaced by low sulfur residual fuels. The results imply that shipping remains as a significant source of anthropogenic PN emissions that should be considered in future climate and health impact models.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53-0.87 (PM_2.5), 0.53-0.85 (PM₁₀), and 0.33-0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM_2.5 and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM_2.5/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM_2.5 and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Predicting outdoor ultrafine particle number concentrations, particle size, and noise using street-level images and audio data

Outdoor ultrafine particles (UFPs) (<0.1 µm) may have an important impact on public health but exposure assessment remains a challenge in epidemiological studies. We developed a novel method of estimating spatiotemporal variations in outdoor UFP number concentrations and particle diameters using street-level images and audio data in Montreal, Canada. As a secondary aim, we also developed models for noise. Convolutional neural networks were first trained to predict 10-second average UFP/noise parameters using a large database of images and audio spectrogram data paired with measurements collected between April 2019 and February 2020. Final multivariable linear regression and generalized additive models were developed to predict 5-minute average UFP/noise parameters including covariates from deep learning models based on image and audio data along with outdoor temperature and wind speed. The best performing final models had mean cross-validation R² values of 0.677 and 0.523 for UFP number concentrations and 0.825 and 0.735 for UFP size using two different test sets. Audio predictions from deep learning models were stronger predictors of spatiotemporal variations in UFP parameters than predictions based on street-level images; this was not explained only by noise levels captured in the audio signal. All final noise models had R² values above 0.90. Collectively, our findings suggest that street-level images and audio data can be used to estimate spatiotemporal variations in outdoor UFPs and noise. This approach may be useful in developing exposure models over broad spatial scales and such models can be regularly updated to expand generalizability as more measurements become available.

Development and transferability of ultrafine particle land use regression models in London

Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm^-3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016-2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R² ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R² ranging from -18 to 0. MSE-R² was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.