Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Greenness modifies the association between ambient air pollution and cognitive function in Australian adolescents, but not in mid-life adults

Exposure to ambient air pollution has been associated with reduced cognitive function in childhood and later life, with too few mid-life studies to draw conclusions. In contrast, residential greenness has been associated with enhanced cognitive function throughout the lifecourse. Here we examine the extent to which (1) ambient air pollution and residential greenness predict later cognitive function in adolescence and mid-life, and (2) greenness modifies air pollution-cognitive function associations.

PARTICIPANTS

6220 adolescents (51% male) and 2623 mid-life adults (96% mothers) from the Longitudinal Study of Australian Children.

MEASURES

Exposures: Annual average particulate matter <2.5 μm (PM_2.5), nitrogen dioxide (NO₂) and greenness (Normalised Difference Vegetation Index) for residential addresses from validated land-use regression models over a 10-13-year period.

OUTCOMES

Cognitive function from CogState tests of attention, working memory and executive function, dichotomised into poorer (worst quartile) versus not poor.

ANALYSES

Adjusted mixed-effects generalised linear models with residential greenness assessed as an effect modifier (high vs. low divided at median). The annual mean for PM_2.5 and NO₂ across exposure windows was 6.3-6.8 μg/m³, and 5.5-7.1 ppb, respectively. For adolescents, an IQR increment of NO₂ was associated with 19-24% increased odds of having poorer executive function across all time windows, while associations weren't observed between air pollution and other outcomes. For adults, high NO₂ predicted poorer cognitive function across all outcomes, while high PM_2.5 predicted poorer attention only. There was little evidence of associations between greenness and cognitive function in adjusted models for both generations. Interactions were found between residential greenness, air pollutants and cognitive function in adolescents, but not adults. The magnitude of effects was similar across generations and exposure windows. Findings highlight the potential benefits of cognitive health associated with the regulation of air pollution and urban planning strategies for increasing green spaces and vegetation.

Perceptions of air quality and concern for health in relation to long-term air pollution exposure, bushfires, and COVID-19 lockdown: A before-and-after study

BACKGROUND

Air pollution is a major health burden and the leading environmental risk factor for non-communicable diseases worldwide. People's perceptions and concerns about air pollution are important as they may predict protective behaviour or support for climate change mitigation policies.

METHODS

This repeat cross-sectional study uses survey data collected from participants in Sydney, Australia in September-November 2019 (n = 1,647) and October-December 2020 (n = 1,458), before and after the devastating 2019/2020 bushfires and first COVID-19 lockdown restrictions in Sydney in 2020. Participants' perceptions of air quality and concerns for health in relation to air quality were modeled against estimates of annual average NO₂ and PM_2.5 concentrations in their neighbourhood.

RESULTS

Participants in suburbs with higher estimated air pollution concentrations generally perceived poorer air quality and were more concerned for health in relation to air quality. A 5 µg/m³ increase in NO₂ was associated with perceived poorer air quality (OR 1.32, 95%CI 1.18-1.47). A 1 µg/m³ increase in estimated PM_2.5 was associated with perceived poorer air quality (OR 1.37, 95%CI 1.24-1.52) and greater concern for health (OR 1.18, 95%CI 1.05-1.32). Air quality was perceived as better in 2020 than in 2019 in both NO₂ and PM_2.5 models (p<0.001). Air quality concern increased in 2020 in both models.

DISCUSSION

This study provides the first Australian data on the association between estimated air quality exposure and air quality perceptions and concerns, contributing new evidence to inform public health approaches that increase awareness for air pollution and reduce the health burden.

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Maternal and Childhood Ambient Air Pollution Exposure and Mental Health Symptoms and Psychomotor Development in Children: An Australian Population-Based Longitudinal Study

BACKGROUND

Accumulating evidence indicates early life exposure to air pollution, a suspected neurotoxicant, is negatively associated with children's neurodevelopment.

OBJECTIVES

To explore the role of multiple exposure periods to ambient particulate matter with diameter <2.5 μm (PM_2.5) and nitrogen dioxide (NO₂) on emotion and behaviour, and early development in children <13 years.

METHODS

We used data from Mothers and their Children's Health (MatCH) study, a 2016/17 sub-study from a prospective longitudinal study, the Australian Longitudinal Study on Women's Health. Annual PM_2.5 and NO₂ estimates since 1996 were obtained from a land-use regression model. Maternal residential proximity to roadways were used as a proxy measure of exposure to traffic-related air pollution. Child outcomes were maternal-rated emotional and behavioural problems (Strengths and Difficulties Questionnaire; SDQ, aged 2-12 years, n = 5471 children) and developmental delay in communication and gross motor skills (Ages and Stages Questionnaire; ASQ, aged 1-66 months, n = 1265 children). Defined exposure periods were early life exposure ('during pregnancy' and 'first year of life') and 'children's lifetime exposure'. Ambient air pollution was divided into tertiles and logistic regression was performed to estimate odds ratio (OR) for each child outcome, adjusting for potential confounders.

RESULTS

Children exposed to moderate and high PM_2.5 exposure, compared to low exposure, across all periods, had higher odds of emotional and behavioural problems, and gross motor delay. Children's lifetime exposure to moderate levels of PM_2.5 (5.9-7.1 µg/m³) was associated with 1.27 (95% confidence interval 1.03, 1.57) fold higher odds of emotional/behavioural problems. Similar associations were found for moderate PM_2.5 levels at 'first year of life' in a two-pollutant model only (OR: 1.30; 1.05, 1.60). However, there was insufficient evidence to suggest that NO₂ exposure or living within 200 m of major roads was associated with emotional and behaviour problems or developmental delay across any exposure periods.

CONCLUSION

We found isolated evidence that early life and childhood exposure to PM_2.5 may be associated with emotional and behavioural problems and delays in gross motor skills, but most associations were null. Due to the limited number of longitudinal studies on low-exposure settings, further studies with more temporally refined exposure assessment are warranted.

Relationship between life-time exposure to ambient fine particulate matter and carotid artery intima-media thickness in Australian children aged 11-12 years

Long-term exposure to air pollutants, especially particulates, in adulthood is related to cardiovascular diseases and vascular markers of atherosclerosis. However, whether vascular changes in children is related to exposure to air pollutants remains unknown. This study examined whether childhood exposure to air pollutants was related to a marker of cardiovascular risk, carotid intima-media thickness (CIMT) in children aged 11-12 years old. Longitudinal Study of Australian Children (LSAC) recruited parents and their children born in 2003-4. Among the participants, CheckPoint examination was conducted when the children were 11-12 years old. Ultrasound of the right carotid artery was performed using standardized protocols. Average and maximum far-wall CIMT, carotid artery distensibility, and elasticity were quantified using semiautomated software. Annual and life-time exposure to air pollutants was estimated using satellite-based land-use regression by residential postcodes. A total of 1063 children (50.4% girls) with CIMT data, serum cholesterol, and modeled estimates of NO₂ and PM_2.5 exposure for the period 2003 to 2015 were included. The average and maximum CIMT, carotid distensibility, and elasticity were 497 μm (standard deviation, SD 58), 580 μm (SD 44), 17.4% (SD 3.2), and 0.48%/mmHg (SD 0.09), respectively. The life-time average concentrations of PM_2.5 and NO₂ were 6.4 μg/m³ (SD 1.4) and 6.4 ppb (SD 2.4), respectively. Both average and maximum CIMT were significantly associated with average ambient PM_2.5 concentration (average CIMT: +5.5 μm per μg/m³, 95% confidence interval, CI 2.4 to 8.5, and maximum CIMT: +4.9 μm per μg/m³, CI 2.3 to 7.6), estimated using linear regression, adjusting for potential confounders. CIMT was not significantly related to NO₂ exposure. Carotid artery diameter, distensibility, and elasticity were not significantly associated with air pollutants. We conclude that life-time exposure to low levels of PM_2.5 in children might have measurable adverse impacts on vascular structure by age 11-12 years.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

BACKGROUND

Long-term surface NO₂ data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO₂ observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift.

OBJECTIVE

This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO₂ levels across China during 2005-2018.

METHODS

On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO₂ through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO₂ levels.

RESULTS

The validation against Taiwan's NO₂ observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m³, 7.1 to 4.3 µg/m³, and 6.1 to 2.9 µg/m³ in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO₂ ([NO₂]_pw) during 2005-2012 was estimated as 40.2 and 50.9 µg/m³ by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO₂]_pw increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO₂ > 40 µg/m³ were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively.

CONCLUSION

With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO₂ across China during 2005-2018, which is valuable for environmental management and epidemiological research.

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.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM_2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM_2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM_2.5 ("SAT-PM_2.5"). We aimed to determine the validity of such satellite-based LUR models for PM_2.5 in Australia. We used global SAT-PM_2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM_2.5 predictor variable (and six others) explained the most spatial variability in PM_2.5 (adjusted R² = 0.63, RMSE (μg/m³ [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R² = 0.52, RMSE: 1.15 [16%]). The evaluation R² of the SAT-PM_2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM_2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM_2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM_2.5 exposure assessment in Australia.