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

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels

Polycyclic aromatic hydrocarbons (PAHs) are an important class of pollutants in China. The land use regression (LUR) model has been used to predict the selected PAH concentrations and screen the key influencing factors. However, most previous studies have focused on particle-associated PAHs, and research on gaseous PAHs was limited. This study measured representative PAHs in both gaseous phases and particle-associated during the windy, non-heating and heating seasons from 25 sampling sites in different areas of Taiyuan City. We established separate prediction models of 15 PAHs. Acenaphthene (Ace), Fluorene (Flo), and benzo [g,h,i] perylene (BghiP) were selected to analyze the relationship between PAH concentration and influencing factors. The stability and accuracy of the LUR models were quantitatively evaluated using leave-one-out cross-validation. We found that Ace and Flo models show good performance in the gaseous phase (Ace: adj. R² = 0.14-0.82; Flo: adj. R² = 0.21-0.85), and the model performance of BghiP is better in the particle phase (adj. R² = 0.20-0.42). Additionally, better model performance was observed in the heating season (adj R² = 0.68-0.83) than in the non-heating (adj R² = 0.23-0.76) and windy seasons (adj R² = 0.37-0.59). Those gaseous PAHs were highly affected by traffic emissions, elevation, and latitude, whereas BghiP was affected by point sources. This study reveals the strong seasonal and phase dependence of PAH concentrations. Building separate LUR models in different phases and seasons improves the prediction accuracy of PAHs.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM_2.5 concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM_2.5 in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM_2.5 exposures. In terms of air pollution spatial modelling, using only a single model might lose information in capturing spatial and temporal correlation between predictors and pollutant levels. Hence, this study aimed to propose an ensemble mixed spatial model that incorporated Kriging interpolation, land-use regression (LUR), machine learning, and stacking ensemble approach to estimate long-term PM_2.5 variations for nearly three decades in daytime and nighttime. Three steps of model development were applied: 1) linear based LUR and Hybrid Kriging-LUR were used to determine influential predictors; 2) machine learning algorithms were used to enhance model prediction accuracy; 3) predictions from the selected machine learning models were fitted and evaluated again to build the final ensemble mixed spatial model. The results showed that prediction performance increased from 0.514 to 0.895 for daily, 0.478 to 0.879 for daytime, and 0.523 to 0.878 for nighttime when applying the proposed ensemble mixed spatial model compared with LUR. Results of overfitting test and extrapolation ability test confirmed the robustness and reliability of the developed models. The distance to the nearest thermal power plant, density of soil and pebbles fields, and funeral facilities might affect the variation of PM_2.5 levels between daytime and nighttime. The PM_2.5 level was higher in daytime compared with nighttime with little difference, revealing the importance of estimating nighttime PM_2.5 variations. Our findings also clarified the emission sources in daytime and nighttime, which serve as valuable information for air pollution control strategies establishment.

Outdoor air pollution exposure and the risk of asthma and wheezing in the offspring

According to the "fetal origin of disease" hypothesis, air pollution exposure in pregnancy may play an important role in stimulating the early programming of asthma and allergies. However, previous studies reported inconsistent findings. The aim of this meta-analysis was to provide higher grade evidence and quantitatively analyze the link between prenatal exposure to outdoor air pollutants and childhood asthma and wheezing. Databases (Web of Science and PubMed) were extensively searched for articles published from the start of the database to September 15, 2021. Either random-effect model or fixed-effect model was used to estimate the disease-specific relative risks (RR) with the corresponding 95% confidence intervals (CIs) to estimate the association. Newcastle-Ottawa Quality Score (NOS) was used to assess the quality of studies. This study finally included 13 cohort studies, and the findings showed that NO₂ and SO₂ exposure during entire pregnancy was significantly associated with wheezing (RR = 1.032, 95% CI: 1.000, 1.066) and asthma (RR = 1.114, 95% CI: 1.066, 1.164), respectively. Further analyses showed that PM_2.5 were positively associated with asthma in the second (RR = 1.194, 95% CI: 1.143, 1.247) and third trimester (RR = 1.050, 95% CI: 1.007, 1.094), while NO₂ (RR = 1.060, 95% CI: 1.021, 1.101) and SO₂ (RR = 1.067, 95% CI: 1.013, 1.123) were shown positively associated with asthma only in the second trimester. The relationship between wheezing and outdoor air pollutants was not significant in any of the pregnancy subgroups. This study suggests that prenatal exposure of outdoor air pollution may increase the asthma and wheezing risk in the offspring and that the second trimester may be a sensitive period for air pollution exposure. But the interpretation of the causal association is hampered by limited number of studies on dose response.

Acute change of lung function to short-term exposure to ambient air pollutants with and without physical activity: A real-world crossover study

Physical activity (PA) would increase the inhalation rate and thereby inhaled dose of air pollutants. However, it's still uncertain whether the effects of air pollutants on lung function are attenuated by PA, especially in the high-polluted areas. We aimed to disentangle the interaction between air pollution and PA on lung function among healthy adults. In this study, a real-world crossover study was conducted among 74 healthy adults. Each participant underwent both rest and 15-min intermittent moderate PA exposure scenarios (consisting of 15min stationary bike riding alternating with 15min of rest), which lasted for 2 h. On the same day, the participants among active and inactive group were exposed to the same air pollution. We have monitored the fine particulate matter (PM_2.5), particulate matter less than 10 μm (PM₁₀), particulate matter less than 1 μm (PM₁), black carbon (BC), nitrogen dioxide (NO₂), and ozone (O₃) continuously during 2-h exposure. Lung function were measured at five times points for each visit (before, immediately, 3 h, 5 h, and 24 h after the 2-h exposure scenario). Mixed-effects models were applied to explore the effects of air pollution, PA, and their interaction on lung function. The participants had a mean (standard deviation (SD)) age of 19.9 (0.9) years. The average concentration [mean ± SD] of PM_2.5, PM₁₀, PM₁, BC, NO₂, and O₃ were 59.4 ± 45.1 μg/m³, 122.8 ± 109.0 μg/m³, 38.8 ± 29.2 μg/m³, 1.94 ± 1.17 μg/m³, 59.5 ± 26.6 μg/m³, and 74.0 ± 30.3 μg/m³, respectively. Overall, greater increasement in lung function were observed among active group compared with inactive group at all timepoints. In fully adjusted models, we observed the benefits of PA and detrimental effects of air pollutants on lung function. Our results suggested that PA, compared to rest, alleviated the detrimental effects of air pollutants on lung function. We also stressed the importance of timing of measurements for capturing association. In conclusion, our observations suggested that PA might alleviate the associations between various pollutant exposures and lung function, which would drive further research towards potential pathway.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

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.