Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis

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.

Exposure to low-level ambient air pollution and the relationship with lung and bladder cancer in older men, in Perth, Western Australia

BACKGROUND

Air pollution is a cause of lung cancer and is associated with bladder cancer. However, the relationship between air pollution and these cancers in regions of low pollution is unclear. We investigated associations between fine particulate matter (PM2.5), nitrogen dioxide, and black carbon (BC), and both these cancers in a low-pollution city.

METHODS

A cohort of 11,679 men ≥65 years old in Perth (Western Australia) were followed from 1996-1999 until 2018. Pollutant concentrations, as a time-varying variable, were estimated at participants' residential addresses using land use regression models. Incident lung and bladder cancer were identified through the Western Australian Cancer Registry. Risks were estimated using Cox proportional-hazard models (age as the timescale), adjusting for smoking, socioeconomic status, and co-pollutants.

RESULTS

Lung cancer was associated with PM2.5 and BC in the adjusted single-pollutant models. A weak positive association was observed between ambient air pollution and squamous cell lung carcinoma but not lung adenocarcinoma. Positive associations were observed with bladder cancer, although these were not statistically significant. Associations were attenuated in two-pollutant models.

CONCLUSION

Low-level ambient air pollution is associated with lung, and possibly bladder, cancer among older men, suggesting there is no known safe level for air pollution as a carcinogen.

Long-term exposure to low-concentration PM2.5 and heart disease in older men in Perth, Australia: The Health in Men Study

Exposure to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is associated with increased risk of heart disease, but less is known about the relationship at low concentrations. This study aimed to determine the dose-response relationship between long-term PM2.5 exposure and risk of incident ischemic heart disease (IHD), incident heart failure (HF), and incident atrial fibrillation (AF) in older men living in a region with relatively low ambient air pollution.

Methods

PM2.5 exposure was estimated for 11,249 older adult males who resided in Perth, Western Australia and were recruited from 1996 to 1999. Participants were followed until 2018 for the HF and AF outcomes, and until 2017 for IHD. Cox-proportional hazards models, using age as the analysis time, and adjusting for demographic and lifestyle factors were used. PM2.5 was entered as a restricted cubic spline to model nonlinearity.

Results

We observed a mean PM2.5 concentration of 4.95 μg/m3 (SD 1.68 μg/m3) in the first year of recruitment. After excluding participants with preexisting disease and adjusting for demographic and lifestyle factors, PM2.5 exposure was associated with a trend toward increased incidence of IHD, HF, and AF, but none were statistically significant. At a PM2.5 concentration of 7 μg/m3 the hazard ratio for incident IHD was 1.04 (95% confidence interval [CI] = 0.86, 1.25) compared with the reference category of 1 μg/m3.

Conclusions

We did not observe a significant association between long-term exposure to low-concentration PM2.5 air pollution and IHD, HF, or AF.

Ambient air pollution and risk of incident dementia in older men living in a region with relatively low concentrations of pollutants: The Health in Men Study

BACKGROUND

In areas with moderate to severe air pollution, pollutant concentrations are associated with dementia risk. It is unclear whether the same relationship is present in regions with lower ambient air pollution.

OBJECTIVE

To determine whether exposure to air pollution is associated with risk of incident dementia in general, and Alzheimer's disease and vascular dementia in particular, in older men living in a relatively low ambient air pollution region.

METHODS

The cohort comprised 11,243 men residing in Perth, Australia. Participants were aged ≥65 years and free of a dementia diagnosis at time of recruitment in 1996-1999. Incident dementia was identified from recruitment to 2018 via ICD diagnosis codes and subsequent study waves. Concentrations for three air pollutants, nitrogen dioxide (NO₂), fine particulate matter less than 2.5 μm in diameter (PM_2.5), and black carbon (BC) were estimated at participants' home addresses using land-use regression models. We used Cox proportional hazards regression models adjusting for smoking status, physical activity, BMI, education, and socio-economic status.

RESULTS

Of 3053 (27.2%) incident cases of dementia, 1670 (54.7%) and 355 (11.6%) had documented Alzheimer's disease and vascular dementia. The average concentration of NO₂ was 13.5 (SD 4.4) μg/m³, of PM_2.5 was 4.54 (SD 1.6) μg/m³ and of BC was 0.97 (SD 0.29) ×10^-5 m^-1. None of the air pollutants were associated with incident dementia or Alzheimer's disease. In the unadjusted model, increased exposure to PM_2.5 was associated with an increased risk of vascular dementia (for a 5 μg/m³ increase: HR 1.62, 95% CI 1.13, 2.31). However, this association was attenuated following adjustment for confounders (HR 1.39, 95% CI 0.93, 2.08). NO₂ and BC were not associated with vascular dementia incidence.

DISCUSSION

Exposure to air pollution is not associated with increased risk of incident dementia in older men living in a region with relatively low ambient air pollution.

Locally weighted total least-squares variance component estimation for modeling urban air pollution

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM_2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method's efficiency in PM_2.5 modeling.

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.

Application of land use regression modelling to describe atmospheric levels of semivolatile organic compounds on a national scale

Despite the success of passive sampler-based monitoring networks in capturing global atmospheric distributions of semivolatile organic compounds (SVOCs), their limited spatial resolution remains a challenge. Adequate spatial coverage is necessary to better characterize concentration gradients, identify point sources, estimate human exposure, and evaluate the effectiveness of chemical regulations such as the Stockholm Convention on Persistent Organic Pollutants. Land use regression (LUR) modelling can be used to integrate land use characteristics and other predictor variables (industrial emissions, traffic intensity, demographics, etc.) to describe or predict the distribution of air concentrations at unmeasured locations across a region or country. While LUR models are frequently applied to data-rich conventional air pollutants such as particulate matter, ozone, and nitrogen oxides, they are rarely applied to SVOCs. The MONET passive air sampling network (RECETOX, Masaryk University) continuously measures atmospheric SVOC levels across Czechia in monthly intervals. Using monitoring data from 29 MONET sites over a two-year period (2015-2017) and a variety of predictor variables, we developed LUR models to describe atmospheric levels and identify sources of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and DDT across the country. Strong and statistically significant (R² > 0.6; p < 0.05) models were derived for PAH and PCB levels on a national scale. The PAH model retained three predictor variables - heating emissions represented by domestic fuel consumption, industrial PAH point sources, and the hill:valley index, a measure of site topography. The PCB model retained two predictor variables - site elevation, and secondary sources of PCBs represented by soil concentrations. These models were then applied to Czechia as a whole, highlighting the spatial variability of atmospheric SVOC levels, and providing a tool that can be used for further optimization of sampling network design, as well as evaluating potential human and environmental chemical exposures.

Spatial analysis and evolution of four air pollutants in England and Wales

Pollution control is based on an exhaustive knowledge of concentration distributions. This study analyses a detailed database of NO₂, O₃, PM₁₀ and PM_2.5 in England and Wales over the period 2007-2011. Daily and annual means were considered in a 1-km spatial resolution. Histograms revealed a shape like a sawtooth. The interval was wide for NO₂ and O₃, although with a gap, whilst the particulate matter range was narrow. Spring provided the peak for the O₃ annual cycle, whereas minima for the other pollutants were reached in summer. A trend for the annual medians of particulate matter was observed, with a minimum in the period analysed. However, the pattern was uniform for NO₂ and O₃. Cities appeared as NO₂ hot spots and O₃ cold spots. Wales stood out as an NO₂ clean country, although with high O₃ levels. Sources or sinks of particulate matter were not observed, suggesting that more detailed analysis is required. Two NO₂ pollution axes were sometimes seen, one in the south from London to Bristol, and the second in the north, from Liverpool to Kingston Upon Hull. No annual spatial pattern was seen for the remaining pollutants beyond the contrast between cities and country sites for O₃. Consequently, spatial analysis allows the real impact of pollutant sources be known, although it must be performed with a detailed temporal resolution in order to investigate the extension, quantity, and length of the concentrations calculated.

Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM₁₀ and PM_2.5), sulphur dioxide (SO₂), and nitrogen dioxide (NO₂) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively). Furthermore, higher levels of NO₂ and SO₂ were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO₂ models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO₂ models were less robust with lower R² annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R² for PM₁₀ models ranged from 52% to 80% while for PM_2.5 models this range was 61–76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO₂ concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.