Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 μg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

A quantitative assessment of the consistency of projections from five mathematical models of the HIV epidemic in South Africa: a model comparison study

BACKGROUND

Mathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa.

METHODS

The five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a "status quo" scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time.

RESULTS

For most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95-95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children.

CONCLUSIONS

While models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies.

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.

A daily and complete PM2.5 dataset derived from space observations for Vietnam from 2012 to 2020

PM_2.5 pollution is a serious problem in Vietnam and around the world, having bad impacts on human health, animals and environment. Regular monitoring at a large scale is important to assess the status of air pollution, develop solutions and evaluate the effectiveness of policy implementation. However, air quality monitoring stations in Vietnam are limited. In this article, we propose an approach to estimate daily PM_2.5 concentration from 2012 to 2020 over the Vietnamese territory, which is strongly affected by cloudy conditions, using a modern statistical model named Mixed Effect Model (MEM) on a dataset consisting of ground PM_2.5 measurements, integrated satellite Aerosol Optical Depth (AOD), meteorological and land use maps. The result of this approach is the first long-term, full coverage and high quality PM_2.5 dataset of Vietnam. The daily mean PM_2.5 maps have high validation results in comparison with ground PM_2.5 measurement (Pearson r of 0.87, R² of 0.75, RMSE of 11.76 μg/m³, and MRE of 36.57 % on a total of 13,886 data samples). The aggregated monthly and annual average maps from 2012 to 2020 in Vietnam have outstanding quality when compared with another global PM_2.5 product. The PM_2.5 concentration maps has shown spatial distribution and seasonal variations of PM_2.5 concentration in Vietnam in a long period from 2012 to 2020 and has been used in other studies and applications in the environment and public health at the national scale, which has not been possible before because of the lack of monitoring stations and an appropriate PM_2.5 modeling approach.

Mortality Burden due to Exposure to Outdoor Fine Particulate Matter in Hanoi, Vietnam: Health Impact Assessment

Objective: This study reports the mortality burden due to PM_2.5 exposure among adults (age >25) living in Hanoi in 2017.

Methods: We applied a health impact assessment methodology with the global exposure mortality model and a PM_2.5 map with 3 × 3 km resolution derived from multiple data sources.

Results: The annual average PM_2.5 concentration for each grid ranged from 22.1 to 37.2 µg/m³. The district average concentration values ranged from 26.9 to 37.2 µg/m³, which means that none of the 30 districts had annual average values below the Vietnam Ambient National Standard of 25 µg/m³. Using the Vietnam Ambient National Standard as the reference standard, we estimated that 2,696 deaths (95% CI: 2,225 to 3,158) per year were attributable to exposure to elevated PM_2.5 concentrations in Hanoi. Using the Interim Target 4 value of 10 µg/m³ as the reference standard, the number of excess deaths attributable to elevated PM_2.5 exposure was 4,760 (95% CI: 3,958–5,534).

Conclusion: A significant proportion of deaths in Hanoi could be avoided by reducing air pollution concentrations to a level consistent with the Vietnam Ambient National Standard.

An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, Central China

As human interference with the natural environment accelerates, land use has undergone great changes. However, to realize rational land development in the rural-urban ecotone, the micro-spatial (MS) unit is the best scale for the management and planning of sustainable land use. Taking Wuhan metropolitan area as research area, the integrated logistic-multi-criteria evaluation (MCE)-cellular automata (CA)-Markov model was used to simulate land use pattern for 2025. In addition, the 1 km×1 km, 2 km×2 km, 3 km×3 km, and 4 km×4 km and typical sample belt were built to reveal the spatial microcosmic expression of land use structure. The results showed that the kappa coefficient and figure of merit (FoM) were 88.01% and 26.86%, respectively, indicating the integration model has high prediction accuracy. In 2005-2025, the diversification of land use in the Wuhan metropolitan area will be generally above the medium level, and the types of land combinations will be relatively abundant. As human activities increase, the land use degree will show increases continuously, it will expand outward from Wuhan, and there is a positive correlation between cultivated land-rural residential land and urban land-cultivated land. The spatial distribution of land use structure presents regional scale characteristics, and different regions have micro-spatial scale dependence. The selection of MS scales based on local conditions can be a good way to reflect land use internal structure and provide a better reference for the compilation of regional land use optimization.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Land use regression modelling of NO2 in São Paulo, Brazil

BACKGROUND

Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce.

METHODS

In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO₂) concentration in the study area of the Western Region Birth Cohort in São Paulo. NO₂ measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction.

RESULTS

Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R²) respectively. Cross-holdout validation tests suggest robust models. NO₂ levels ranged from 43.2 μg/m³ to 93.4 μg/m³ in the winter and between 28.1 μg/m³ and 72.8 μg/m³ in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO₂ values over the WHO suggested annual guideline of 40 μg/m³ annual average.

CONCLUSION

In this study we were able to develop robust models to predict NO₂ residential exposure. We could show that average measures, and therefore the predictions of NO_2, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO₂ levels.

Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments

Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.

Effective reduction of roadside air pollution with botanical biofiltration

Currently no sustainable, economical and scalable systems have been developed for the direct removal of roadside air pollutants at their source. Here we present a simple and effective air filtering technology: botanical biofiltration, and the first field assessment of three different botanical biofilter designs for the filtration of traffic associated air pollutants - NO₂, O₃ and PM_2.5 - from roadside ambient air in Sydney, Australia. Over two six month research campaigns, we show that all of the tested systems filtered NO₂, O₃ and PM_2.5 with average single pass removal efficiencies of up to 71.5%, 28.1% and 22.1% respectively. Clean air delivery rates of up to 121 m³/h, 50 m³/h and 40 m³/h per m² of active green wall biofilter were achieved for the three pollutants respectively, with pollutant removal efficiency positively correlated with their ambient concentrations. We propose that large scale field trials of this technology are warranted to promote sustainable urban development and improved public health outcomes.

Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model

INTRODUCTION

To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality.

METHODS

We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models.

RESULTS

Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61-0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2.

CONCLUSION

Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

The impact of measurement error in modeled ambient particles exposures on health effect estimates in multilevel analysis: A simulation study

Supplemental Digital Content is available in the text.

Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM₁₀) and <2.5 µm (PM_2.5) concentrations on the estimation of health effects.

A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

This paper presents a summary of the key findings of the special issue of Atmosphere on Air Quality in New South Wales and discusses the implications of the work for policy makers and individuals. This special edition presents new air quality research in Australia undertaken by (or in association with) the Clean Air and Urban Landscapes hub, which is funded by the National Environmental Science Program on behalf of the Australian Government’s Department of the Environment and Energy. Air pollution in Australian cities is generally low, with typical concentrations of key pollutants at much lower levels than experienced in comparable cities in many other parts of the world. Australian cities do experience occasional exceedances in ozone and PM2.5 (above air pollution guidelines), as well as extreme pollution events, often as a result of bushfires, dust storms, or heatwaves. Even in the absence of extreme events, natural emissions play a significant role in influencing the Australian urban environment, due to the remoteness from large regional anthropogenic emission sources. By studying air quality in Australia, we can gain a greater understanding of the underlying atmospheric chemistry and health risks in less polluted atmospheric environments, and the health benefits of continued reduction in air pollution. These conditions may be representative of future air quality scenarios for parts of the Northern Hemisphere, as legislation and cleaner technologies reduce anthropogenic air pollution in European, American, and Asian cities. However, in many instances, current legislation regarding emissions in Australia is significantly more lax than in other developed countries, making Australia vulnerable to worsening air pollution in association with future population growth. The need to avoid complacency is highlighted by recent epidemiological research, reporting associations between air pollution and adverse health outcomes even at air pollutant concentrations that are lower than Australia’s national air quality standards. Improving air quality is expected to improve health outcomes at any pollution level, with specific benefits projected for reductions in long-term exposure to average PM2.5 concentrations.

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.