Land use regression models for estimating individual NOx and NO₂ exposures in a metropolis with a high density of traffic roads and population

Associations among air pollution, asthma and lung function: a cross-sectional study

Ambient air pollution affects the respiratory system, but evidence of its impacts on asthma and lung function is lacking. We aimed to evaluate whether ambient air pollutants are associated with asthma prevalence, asthma outcomes, and lung function in adults. A cross-sectional study of 454,921 adults aged 37 to 73 years from the UK Biobank was performed with linear or logistic regression to assess the associations among air pollution and asthma prevalence, current wheezing, asthma hospitalizations and lung function. Each interquartile range (IQR) increase in of PM2.5 (odds ratio [OR]: 1.023, 95% confidence interval [CI]: 1.011-1.035), PM10 (OR: 1.013, 95% CI: 1.004-1.022), NO2 (OR: 1.025, 95% CI: 1.013-1.039), and NOx (OR: 1.019, 95% CI: 1.008-1.029) was significantly associated with asthma prevalence, respectively. Moreover, exposure to air pollution was related to increased odds of current wheezing and asthma-related hospitalization. Among asthmatic participants, each IQR increase in PMcoarse, PM10, NO2, and NOx was significantly associated with decreases of 5.143 ml, 7.614 ml, 13.266 ml, 9.440 ml, respectively, for the forced expiratory volume in one second and 11.744 ml, 15.637 ml, 13.041 ml, 9.063 ml, respectively, for the forced vital capacity. In a large sample size study of British adults, air pollution was related to increased odds of asthma prevalence. Among the asthmatic population, air pollution was associated with increased odds of current wheezing, hospitalization, and decreased lung function.

The association between ambient air pollution and colorectal cancer: a Mendelian randomization study

Mounting epidemiology studies have reported the potential associations between ambient air pollution exposure and colorectal cancer (CRC). However, the genetic association between ambient air pollution and CRC remains unclear. Using the Genome-wide association study (GWAS) data from UK biobank, we explored the genetic association of CRC (5,657 cases and 372,016 controls) with four ambient air pollutants (PM2.5, PM10, NO2, NOx; n = 423,796 to 456,380) under the framework of Mendelian randomization (MR). Our results revealed a significant association between long-term NO2 exposure (per 10 µg/m3) and increased CRC risk, with an odds ratio (OR) of 1.02 (95% confidence interval [CI]: 1.00-1.03), while no statistical association was found between CRC risk and the other air pollutants. Sensitivity analysis confirmed the robustness of the results. It is imperative to consider the impact of air pollution, particularly NO2, in mitigating the risk of CRC.

Ambient air pollution and risk of allergic respiratory diseases in European and East Asian populations: A Mendelian randomization study

BACKGROUND

Ambient air pollution has become a challenging global health issue since industrialization, especially affecting respiratory diseases. However, the causal link between air pollution and allergic respiratory diseases (ARDs) remains unclear due to confounding factors in conventional epidemiological studies across different populations. Thus, we aimed to clarify the causal associations between air pollution and ARDs in European and East Asian populations using Mendelian randomization (MR).

METHODS

MR utilizes genetic variants and provides a satisfactory level of causal evidence. Genetic data for exposures (PM2.5, PM2.5 absorbance, PM10, PMcoarse, NO2 and NOx) and outcomes (allergic rhinitis, chronic rhinosinusitis, asthma, and obesity related asthma) were obtained from genome-wide association studies. Instrumental variables were strictly filtered based on core assumptions. Two-sample MR and sensitivity analyses were conducted separately for European and East Asian populations.

RESULTS

PMcoarse was causally associated with an increased risk of chronic rhinosinusitis (OR = 1.588 [1.002-2.518]; p = 0.049) and obesity related asthma (OR = 1.956 [1.012-3.780]; p = 0.046) in European population, and PM10 was associated with a decreased risk of allergic rhinitis in East Asian population (OR = 0.882 [0.798-0.974]; p = 0.013). No heterogeneity or pleiotropy was detected in any significant causal association.

CONCLUSION

Our findings indicate that ambient air pollution has opposite impacts on the etiology of ARDs in European and East Asian populations, which provides evidence for decisions on public policies and suggests that different responses to environmental factors such as air pollution may contribute to racial heterogeneity of ARDs.

Long-term exposure to air pollutants and new-onset migraine: A large prospective cohort study

BACKGROUNDS

Short-term exposure to air pollutants increases the risk of migraine, but the long-term impacts of exposure to multiple pollutants on migraine have not been established. The aim of this large prospective cohort study was to explore these links.

METHODS

A total of 458,664 participants who were free of migraine at baseline from the UK Biobank were studied. Cox proportional hazards models were used to estimate the risk of new-onset migraine from combined long-term exposure to four pollutants, quantified as an air pollution score using principal component analysis.

RESULTS

During a median (IQR) follow-up of 12.5 (11.8, 13.2) years, a total of 5417 new-onset migraine cases were documented. Long-term exposure to multiple air pollutants was associated with an increased risk of new-onset migraine, as indicated by an increased in the SDs of PM2.5 (hazard ratio (HR): 1.04, 95% CI: 1.01-1.06, P = 0.009), PM10 (HR: 1.07, 95% CI: 1.04-1.10, P < 0.001), NO2 (HR: 1.10, 95% CI: 1.07-1.13, P < 0.001) and NOx (HR: 1.04, 95% CI: 1.01-1.07, P = 0.005) in the main model. The air pollution score showed a doseresponse association with an increased risk of new-onset migraine. Similarly, compared with those of the lowest tertile, the HRs (95% CI) of new-onset migraine were 1.11 (95% CI: 1.04-1.19, P = 0.002) and 1.17 (95% CI: 1.09-1.26, P < 0.001) in tertiles 2 and 3, respectively, according to the main model (P trend < 0.001).

CONCLUSION

Long-term individual and joint exposure to multiple air pollutants is associated with an increased risk of new-onset migraine.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.

Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies

High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classification model, to automatically classify Google Earth images into pixel-based land cover maps. By combining domain adaptation (DA) and semi-supervised learning (SSL) techniques, our model demonstrates its effectiveness even when trained with a limited dataset derived from Gaofen2 (GF2) satellite images. The model's overall accuracy on the translated GF2 dataset improved significantly from 19.5% to 75.2%, and on the Google Earth image dataset from 23.1% to 61.5%. The overall accuracy is 2.9% and 3.4% higher than when using only DA. Furthermore, with this model, we derived land cover maps and investigated the impact of land surface composition on the local meteorological parameters and air pollutant concentrations in the three most developed urban agglomerations in China, i.e., Beijing, Shanghai and the Great Bay Area (GBA). Our correlation analysis reveals that air temperature exhibits a strong positive correlation with neighboring artificial impervious surfaces, with Pearson correlation coefficients higher than 0.6 in all areas except during the spring in the GBA. However, the correlation between air pollutants and land surface composition is notably weaker and more variable. The primary contribution of this paper is to provide an efficient method for urban land cover extraction which will be of great value for assessing the urban surface composition, quantifying the impact of land use/land cover, and facilitating the development of informed policies.

Mendelian-randomization study reveals causal relationships between nitrogen dioxide and gut microbiota

Exposure to nitrogen dioxide might potentially change the makeup and operation of gut microbes. Nitrogen dioxide data was procured from the IEU Open GWAS (N = 456 380). Subsequently, a two-sample Mendelian randomization study was executed, utilizing summary statistics of gut microbiota sourced from the most expansive available genome-wide association study meta-analysis, conducted by the MiBioGen consortium (N = 13 266). The causal relationship between nitrogen dioxide and gut microbiota was determined using inverse variance weighted, maximum likelihood, MR-Egger, Weighted Median, Weighted Model, Mendelian randomization pleiotropy residual sum and outlier, and constrained maximum likelihood and model averaging and Bayesian information criterion. The level of heterogeneity of instrumental variables was quantified by utilizing Cochran's Q statistic. The colocalization analysis was used to examine whether nitrogen dioxide and the identified gut microbiota shared casual variants. Inverse variance weighted estimate suggested that nitrogen dioxide was causally associated with Akkermansia (β = -1.088, 95% CI: -1.909 to -0.267, P = 0.009). In addition, nitrogen dioxide presented a potential association with Bacteroides (β = -0.938, 95% CI: -1.592 to -0.284, P = 0.005), Barnesiella (β = -0.797, 95% CI: -1.538 to -0.055, P = 0.035), Coprococcus 3 (β = 1.108, 95% CI: 0.048-2.167, P = 0.040), Eubacterium hallii group (E. hallii) (β = 0.776, 95% CI: 0.090-1.463, P = 0.027), Holdemania (β = -1.354, 95% CI: -2.336 to -0.372, P = 0.007), Howardella (β = 1.698, 95% CI: 0.257-3.139, P = 0.021), Olsenella (β = 1.599, 95% CI: 0.151-3.048, P = 0.030) and Sellimonas (β = -1.647, 95% CI: -3.209 to -0.086, P = 0.039). No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. The associations of nitrogen dioxide with Akkermansia (PH4 = 0.836) and E. hallii (PH4 = 0.816) were supported by colocalization analysis. This two-sample Mendelian randomization study found that increased exposure to nitrogen dioxide had the potential to impact the human gut microbiota.

Alveolar deposition of inhaled fine particulate matter increased risk of severity of pulmonary tuberculosis in the upper and middle lobes

Inhaled PM_2.5 associated with pulmonary tuberculosis https://bit.ly/3VXAKfq.

Association of air pollution, genetic risk, and lifestyle with incident adult-onset asthma: A prospective cohort study

BACKGROUND

Numerous studies have explored the association of air pollution with asthma but have yielded conflicting results. The exact role of air pollution in the incidence of adult-onset asthma and whether this effect is modified by genetic risk, lifestyle, or their interaction remain uncertain.

METHODS

We conducted a prospective cohort study on 298,738 participants (aged 37-73 years) registered in the UK Biobank. Cox proportional hazard models were used to evaluate the association of air pollution, including particulate matter (PM_2.5, PM_coarse, and PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x), with asthma incidence. We constructed genetic risk and lifestyle scores, assessed whether the impact of air pollution on adult-onset asthma risk was modified by genetic susceptibility or lifestyle factors, and evaluated the identified interactions.

RESULTS

We found that each interquartile range increase in annual concentrations of PM_2.5, NO₂, and NO_x was related to 1.04 (95% confidence interval [CI]: 1.01, 1.08), 1.04 (95% CI: 1.00, 1.08), and 1.03 (95% CI: 1.00, 1.06) times the risk of adult-onset asthma, respectively. The size of the effect of air pollution was greater among subpopulations with low genetic risk or unfavorable lifestyles. We also identified an additive interaction effect of air pollution with lifestyle factors, but not with genetic risk, on the risk of adult-onset asthma.

CONCLUSION

Our analyses show that air pollution increases the risk of adult-onset asthma, but that the size of the effect is modified by lifestyle and genetic risk. These findings emphasize the need for integrated interventions for environmental pollution by the government as well as adherence to healthy lifestyles to prevent adult-onset asthma.

Urban Air Pollution and Subclinical Atherosclerosis in Adolescents and Young Adults

PURPOSE

The contribution of air pollution to subclinical atherosclerosis in a young population remains limited. This study aimed to assess whether long-term exposure to urban air pollutants increases carotid intima-media thickness (CIMT) in adolescents and young adults.

METHODS

This study included 789 subjects between the ages of 12 and 30 years who lived in the Taipei metropolis from a cohort of young Taiwanese individuals. Residential addresses were geocoded, and annual average concentrations of particulate matter (PM) of different diameters, e.g., PM₁₀, PM_2.5-10, PM_2.5, and nitrogen oxides (NO_X), were assessed using land use regression models. The generalized least squares strategy with error term to consider the cluster effect of living addresses between individuals was used to examine the associations between urban air pollution and CIMTs.

RESULTS

After adjusting for potential confounders, we found that interquartile range increases in PM_2.5 (8.2 μg/m³) and NO_X (17.5 μg/m³) were associated with 0.46% (95% CI: 0.02-0.90) and 1.00% (95% CI: 0.10-1.91) higher CIMTs, respectively. Stratified analyses showed that the relationships between CIMT and PM_2.5 and NO_X were more evident in subjects who were 18 years or older, female, nonsmoking, nonhypertensive, and nonhyperglycemic than in their respective counterparts.

DISCUSSION

Long-term exposure to PM_2.5 and NO_X is associated with subclinical atherosclerosis in a young population. Age, sex, and health status may influence the vulnerability of air pollution-associated subclinical atherosclerosis.

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.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), fine particulate matter (PM_2.5), and three speciated PM_2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO₄)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO₂ and PM_2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM_2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO₂, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

Particulate matter (PM) is regarded a major problem worldwide because of the harm it causes to human health. Concentrations of PM with particle diameter less than 2.5 µm (PM2.5) and with particle diameter less than 10 µm (PM10) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM2.5 and PM10 monitoring stations are few, the ability to estimate concentrations at any location based on its environment will benefit healthcare policymakers. This research aimed to study the influence of land use, traffic load, and meteorological factors on the PM2.5 and PM10 concentrations in Bangkok using a land-use regression (LUR) approach. The backward stepwise selection method was applied to select the significant variables to be included in the resultant models. Results showed that the adjusted coefficient of determination of the PM2.5 and PM10 LUR models were 0.58 and 0.57, respectively, which are in the same range as reported in the previous studies. The meteorological variables included in both models were rainfall and air pressure; wind speed contributed to only the PM2.5 LUR model. Further, the land-use types selected in the PM2.5 LUR model were industrial and transportation areas. The PM10 LUR model included residential, commercial, industrial, and agricultural areas. Traffic load was excluded from both models. The root mean squared error obtained by 10-fold cross validation was 9.77 and 16.95 for the PM2.5 and PM10 LUR models, respectively.

Sampling Low Air Pollution Concentrations at a Neighborhood Scale in a Desert U.S. Metropolis with Volatile Weather Patterns

Background: Neighborhood-scale air pollution sampling methods have been used in a range of settings but not in low air pollution airsheds with extreme weather events such as volatile precipitation patterns and extreme summer heat and aridity—all of which will become increasingly common with climate change. The desert U.S. metropolis of Tucson, AZ, has historically low air pollution and a climate marked by volatile weather, presenting a unique opportunity. Methods: We adapted neighborhood-scale air pollution sampling methods to measure ambient NO₂, NO_x, and PM_2.5 and PM₁₀ in Tucson, AZ. Results: The air pollution concentrations in this location were well below regulatory guidelines and those of other locations using the same methods. While NO₂ and NO_x were reliably measured, PM_2.5 measurements were moderately correlated with those from a collocated reference monitor (r = 0.41, p = 0.13), potentially because of a combination of differences in inlet heights, oversampling of acutely high PM_2.5 events, and/or pump operation beyond temperature specifications. Conclusion: As the climate changes, sampling methods should be reevaluated for accuracy and precision, especially those that do not operate continuously. This is even more critical for low-pollution airsheds, as studies on low air pollution concentrations will help determine how such ambient exposures relate to health outcomes.

Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NO_x levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NO_x (n = 428) and NO₂ (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO₂ concentrations. NO and NO₂ concentrations were highest in commercial/business/industrial (66 and 76 μg/m³, respectively) and high-density residential areas (47 and 59 μg/m³, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 μg/m³ for NO and NO₂, and a clear seasonal variation, with the mean NO₂ of 63 μg/m³ (non-Harmattan) increased by 25-56% to 87 μg/m³ (Harmattan) across different site types. The NO₂/NO_x ratio was also elevated by 19-28%. Both NO and NO₂ levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO₂ and NO₂/NO_x and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

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.

Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area

High-resolution air quality maps are critical towards assessing and understanding exposures to elevated air pollution in dense urban areas. However, these surfaces are rarely available in low- and middle-income countries that suffer from some of the highest air pollution levels worldwide. In this study, we make use of land use regressions (LURs) to generate annual and seasonal, high-resolution nitrogen dioxide (NO₂), nitrogen oxides (NO_x), and ozone (O₃) exposure surfaces for the Greater Beirut Area (GBA) in Lebanon. NO₂, NO_x and O₃ concentrations were monitored using passive samplers that were deployed at 55 pre-defined monitoring locations. The average annual concentrations of NO₂, NO_x, and O₃ across the GBA were 36.0, 89.7, and 26.9 ppb, respectively. Overall, the performance of the generated models was appropriate, with low biases, high model robustness, and acceptable R² values that ranged between 0.66 and 0.73 for NO₂, 0.56 and 0.60 for NO_x, and 0.54 and 0.65 for O₃. Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO₂ and NO_x levels in the GBA, whereas they acted as sinks for O₃ concentrations. No seasonally significant differences were found for the NO₂ and NO_x pollution surfaces; as their seasonal and annual models were largely similar (Pearson's r > 0.85 for both pollutants). On the other hand, seasonal O₃ pollution surfaces were significantly different. The model results showed that around 99% of the population of the GBA were exposed to NO₂ levels that exceeded the World Health Organization defined annual standard.

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 > 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

Measurements of NOx and Development of Land Use Regression Models in an East-African City

Air pollution causes premature mortality and morbidity globally, but these adverse health effects occur over proportionately in low- and middle-income countries. Lack of both air pollution data and knowledge of its spatial distribution in African countries have been suggested to lead to an underestimation of health effects from air pollution. This study aims to measure nitrogen oxides (NOx), as well as nitrogen dioxide (NO2), to develop Land Use Regression (LUR) models in the city of Adama, Ethiopia. NOx and NO2 was measured at over 40 sites during six days in both the wet and dry seasons. Throughout the city, measured mean levels of NOx and NO2 were 29.0 µg/m3 and 13.1 µg/m3, respectively. The developed LUR models explained 68% of the NOx variances and 75% of the NO2. Both models included similar geographical predictor variables (related to roads, industries, and transportation administration areas) as those included in prior LUR models. The models were validated by using leave-one-out cross-validation and tested for spatial autocorrelation and multicollinearity. The performance of the models was good, and they are feasible to use to predict variance in annual average NOx and NO2 concentrations. The models developed will be used in future epidemiological and health impact assessment studies. Such studies may potentially support mitigation action and improve public health.

A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter (PM₁₀). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO₂, SO₂, and PM₁₀ concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO₂ and SO₂ were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM₁₀ were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO₂ models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO₂ models were less robust with lower R² annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R² for PM₁₀ models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

A hybrid approach to predict daily NO2 concentrations at city block scale

Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015-2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75-0.79, RMSE: 3.9-4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.

Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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.

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.

A Nonlinear Land Use Regression Approach for Modelling NO2 Concentrations in Urban Areas—Using Data from Low-Cost Sensors and Diffusion Tubes

Land Use Regression (LUR) based on multiple linear regression model is one of the techniques used most frequently for modelling the spatial variability of air pollution and assessing exposure in urban areas. In this paper, a nonlinear generalised additive model is proposed for LUR and its performance is compared to a linear model in Sheffield, UK for the year 2019. Pollution models were estimated using NO2 measurements obtained from 188 diffusion tubes and 40 low-cost sensors. Performance of the models was assessed by calculating several statistical metrics including correlation coefficient (R) and root mean square error (RMSE). High resolution (100 m × 100 m) maps demonstrated higher levels of NO2 in the city centre, eastern side of the city and on major roads. The results showed that the nonlinear model outperformed the linear counterpart and that the model estimated using NO2 data from diffusion tubes outperformed the models using data from low-cost sensors or both low-cost sensors and diffusion tubes. The proposed method provides a basis for further application of advanced nonlinear modelling approaches to constructing LUR models in urban areas which enable quantifying small scale variability in pollution levels.

A hybrid kriging/land-use regression model with Asian culture-specific sources to assess NO2 spatial-temporal variations

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO₂), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO₂ exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO₂ levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO₂ observations from 73 monitoring stations across Taiwan, a set of interpolated NO₂ values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO₂ and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO₂ variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO₂, which can be particularly useful for Asian countries.

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis

The Chinese government has identified air pollution transmission points in Beijing–Tianjin–Hebei region and its surrounding areas under 2 + 26 initiative. This study introduces a modified Gravity Model to construct the spatial correlation network of industrial NOx in 2 + 26 policy region from 2011 to 2015, and further explores network characteristics and socioeconomic factors of this spatial correlation network by Social Network Analysis. Results indicate significant correlation of industrial NOx emission in 2 + 26 policy cities. The spatial correlation network of industrial NOx has remained stable within 5 years, implying no pollution exacerbation of interregional transmission. According to the effect of output and input in the correlation network of industrial NOx, cities in 2 + 26 policy region can be categorized into four types: high-high, high-low, low-low, and low-high, as each should adopt the corresponding strategies for emission reduction. Shijiazhuang, Liaocheng, Cangzhou, Heze and Handan should be key monitored during implementation of emission reduction. Taiyuan, Hebi, Langfang, Tangshan and Yangquan, should give priority to local emission reduction although less associated with other cities, based on city type and current emission situation. Environmental regulation and geographical distance have significant influence on the spatial correlation network of industrial NOx, of which the indicator of environmental regulation difference matrix has become significantly negative since 2014, while the indicator of geographical effect has been significantly positive all along. Urban industrial emission has significant correlation between cities with distance of 0–300 km, while no significant correlation between cities with distance exceeding 300 km.

Cr3+ substituted Zn-Al layered double hydroxides as UV-Vis light photocatalysts for NO gas removal from the urban environment

The ZnAl-CO₃, ZnAlCr-CO₃ and ZnCr-CO₃ LDH samples were studied as De-NOx photocatalysts in this work. Samples without Cr and increasing the presence of Cr³⁺ in the LDH framework in the 0.06, 0.15 and 0.3 Cr/Zn ratio were prepared by co-precipitation method, all of them constituted by pure LDH phase. The increase of chromium content in the LDH framework leads to lower crystallinity and higher specific surface area in the samples. Moreover, the CrO₆ octahedron centres expand the photo-activity from UV to Visible light and assist to decrease the recombination rate of the electrons and holes. The favourable textural, optical and electronic properties of Cr-containing LDH samples explain the good NO removal efficiency (55%) and outstanding selectivity (90%) found for the analysed De-NOx process.

Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations

BACKGROUND

Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data.

OBJECTIVE

Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study.

METHOD

Two one-week integrated samples were collected at 30 PM2.5 (BC) sites and 45 NO2 sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model.

RESULTS

LUR explained 65% of the spatial variability in PM2.5, 78% in BC and 73% in NO2. Mean (±Standard Deviation) of predicted PM2.5, BC and NO2 exposure levels were 48.3 (±6.3) μg/m3, 7.5 (±1.4) μg/m3 and 27.3 (±8.2) μg/m3, respectively. Weak spatial corrections (Pearson r = 0.05-0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM2.5, BC and NO2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO2 levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100-700m) were found for BC and NO2.

CONCLUSION

We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM2.5, NO2 and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM2.5, NO2 and BC concentrations.

A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

BACKGROUND

Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings.

OBJECTIVE

A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings.

METHODS

In each of four seasons in 2016-2017, NO₂ was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM_2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO₂ and PM_2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models.

RESULTS

We developed robust LUR models at the ground level for estimated annual averages of NO₂ (R²: 0.71, adjusted R²: 0.67, and Leave-One-Out Cross Validation (LOOCV) R²: 0.64) and PM_2.5 (R²: 0.77, adjusted R²: of 0.73, and LOOCV R²: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO₂ showed similar patterns. Vertical variation of NO₂ and PM_2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations.

CONCLUSIONS

Ground-level NO₂ and PM_2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Mortality and Morbidity in a Population Exposed to Emission from a Municipal Waste Incinerator. A Retrospective Cohort Study

In the present research, we evaluated the health effects of exposure to the municipal waste incinerator (MWI) in Pisa, Italy, through a population-based cohort design. The individual exposure pattern in the area was estimated through CALPUFF dispersion models of NO_χ (developed by Atmospheric Studies Group Earth Tech, Lowell, Massachusetts), used as pollution proxies of the MWI and the relevant industrial plant, and through land-use regression for NO_χ due to traffic pollution. Using Cox regression analysis, hazard ratios (HR) were estimated adjusting for exposure to other sources of pollution, age, and socioeconomic deprivation. An adjusted linear trend of HR (HRt) over the categories of exposure, with the relative 95% CI and p-value, was also calculated. Mortality and hospital discharge were studied as impact outcomes. Mortality analysis on males showed increased trends of mortality due to natural causes (HRt p < 0.05), the tumor of the lymphohematopoietic system (HRt p = 0.01), cardiovascular diseases (HRt p < 0.01); in females, increased trends for acute respiratory diseases (HRt p = 0.04). Morbidity analysis showed a HRt for lymphohematopoietic system tumor in males (HRt p = 0.04). Some of the excesses are in agreement with previous evidence on the health effects of MWIs, although the observation in males but not in females, suggests a cautious interpretation. Confounding due to other sources of exposure cannot be ruled out. The evidence was considered important in the decision-making process of the waste cycle.

Determining broad scale associations between air pollutants and urban forestry: A novel multifaceted methodological approach

Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂, SO₂, and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.

Exposure to ambient air pollution and risk of childhood cancers: A population-based study in Tehran, Iran

The relationship between air pollution and childhood cancer is inconclusive. We investigated the associations between exposure to ambient air pollution and childhood cancers in Tehran, Iran. This project included children between 1 and 15 years-of-age with a cancer diagnosis by the Center for the Control of Non Communicable Disease (n = 161) during 2007 to 2009. Controls were selected randomly within the city using a Geographic Information System (GIS) (n = 761). The cases were geocoded based on exact home addresses. Air pollution exposure of cases and random controls were estimated by a previously developed Land Use Regression (LUR) model for the 2010 calendar year. The annual mean concentrations of Particulate Matter ≤ 10 μm (PM₁₀), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) in the locations of cancer cases were 101.97 μg/m³, 49.42 ppb and 38.92 ppb respectively, while in the random control group, respective mean exposures were 98.63 μg/m³, 45.98 ppb and 38.95 ppb. A logistic regression model was used to find the probability of childhood cancer per unit increase in PM₁₀, NO₂ and SO₂. We observed a positive association between exposures to PM₁₀ with childhood cancers. We did, however, observe a positive, but not statistically significant association between NO₂ exposure and childhood cancer. Our study is the first to highlight an association between air pollution exposure and childhood cancer risk in Iran, however these findings require replication through future studies.

Spatial and Temporal Variations of Six Criteria Air Pollutants in Fujian Province, China

Air pollution has become a critical issue in the urban areas of southeastern China in recent years. A complete understanding of the tempo-spatial characteristics of air pollution can help the public and governmental bodies manage their lives and work better. In this study, data for six criteria air pollutants (including particulate matter (PM_2.5, PM₁₀), carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and ozone (O₃)) from 37 sites in nine major cities within Fujian Province, China were collected between January 2015 to December 2016, and analyzed. We analyzed the spatial and temporal variations of these six criteria pollutants, as well as the attainment rates, and identified what were the major pollutants. Our results show that: (1) the two-year mean values of PM_2.5 and PM₁₀ exceeded the Chinese National Ambient Air Quality Standard (CAAQS) standard I levels, whereas other air pollutants were below the CAAQS standard I; (2) the six criteria air pollutants show spatial variations (i.e. most air pollutants were higher in the city center areas, followed by suburban areas and exurban areas, except for O₃; and the concentrations of PM₁₀, PM_2.5, NO₂, O₃ were higher in coastal cities than in inland cities); (3) seasonal variations and the no attainment rates of air pollutants were found to be higher in cold seasons and lower in warm seasons, except for O₃; (4) the most frequently present air pollutant was PM₁₀, with PM_2.5 and O₃ being the second and third most frequent, respectively; (5) all the air pollutants, except O₃, showed positive correlations with each other. These results provide additional information for the effective control of air pollution in the province of Fujian.

Land Use Regression Modelling of Outdoor NO₂ and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO₂ and PM_2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO₂ and PM_2.5 were 22.1 μg/m³ and 10.2 μg/m³, respectively. The NO₂ models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R²). The PM_2.5 annual models had lower explanatory power (R² = 0.36, 0.29, and 0.29). The best predictors for NO₂ were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM_2.5, together with population density. This study demonstrates that land-use-regression modelling for NO₂ can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM_2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO₂ and PM_2.5 seasonal exposure estimates and maps for further health studies.

Traffic-related air pollution associated with chronic kidney disease among elderly residents in Taipei City

The associations of air pollution with chronic kidney disease (CKD) have not yet been fully studied. We enrolled 8,497 Taipei City residents older than 65 years and calculated the estimated glomerular filtration rate (eGFR) using the Taiwanese Chronic Kidney Disease Epidemiology Collaboration equation. Proteinuria was assessed via dipstick on voided urine. CKD prevalence and risk of progression were defined according to the KDIGO 2012 guidelines. Land-use regression models were used to estimate the participants' one-year exposures to PM of different sizes and traffic-related exhaust, PM_2.5 absorbance, nitrogen dioxide (NO₂), and NO_x. Generalized linear regressions and logistic regressions were used to examine the associations of one-year air pollution exposures with eGFR, proteinuria, CKD prevalence and risk of progression. The results showed that the interquartile range (IQR) increments of PM_2.5 absorbance (0.4 × 10^-5/m) and NO₂ (7.0 μg/m³) were associated with a 1.07% [95% confidence interval (CI): 0.54-1.57] and 0.84% (95% CI: 0.37-1.32) lower eGFR, respectively; such relationships were magnified in subjects who had an eGFR >60 ml/min/1.73 m² or who were non-diabetic. Similar associations were also observed for PM₁₀ and PM_2.5-10. Two-pollutant models showed that PM₁₀ and PM_2.5 absorbance were associated with a lower eGFR. The odd ratios (ORs) of CKD prevalence and risk of progression also increased with exposures to PM_2.5 absorbance and NO_2. In summary, one-year exposures to traffic-related air pollution were associated with lower eGFR, higher CKD prevalence, and increased risk of CKD progression among the elderly population. Air pollution-related impaired renal function was stronger in non-CKD and non-diabetic subjects.

Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

BACKGROUND

The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of oxides of nitrogen (NO_x) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area.

METHODS

Ambient NO_x was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NO_x exposure of a sample of pregnant women living in South Durban.

RESULTS

Measured NO_x levels ranged from 22.3-50.9μg/m³ with a median of 36μg/m³. The model developed accounts for 73% of the variance in ambient NO_x measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R² of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NO_x ranged from 19.9-53.2μg/m³, with a mean of 39μg/m³.

DISCUSSION AND CONCLUSION

This is the first study to develop a land use regression model that predicts ambient concentrations of NO_x in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NO_x that can be attributed mainly to traffic.

Development of land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing, China

Ambient air pollution has been a global problem, especially in China. Comparing with other methods, Land Use Regression (LUR) models can obtain air pollutant concentration distribution at finer scale without the air pollution source data based on a few monitoring sites and predictors. However, limited LUR studies have been conducted on the basis of regular monitoring networks. Thus, we explored the applicability of conducting LUR models for four key air pollutants: PM_2.5, SO₂, NO₂ and O₃, on the basis of national monitoring networks which have good representation of areas with different characteristics in Nanjing, China. Fifty-nine potential predictor variables were considered, including land use type, population density, traffic emission, industrial emission, geographical coordinates, meteorology and topography. LUR models of these four air pollutants were with good explained variance for four key air pollutants. Adjusted explained variance of the LUR models was highest for NO₂ (87%), followed by SO₂ (83%), and was lower for PM_2.5 (72%) and O₃ (65%). Annual average distributions of pollutants in 2013 were obtained based on predicted values, which revealed that O₃ in Nanjing was more heavily impacted by regional influences. This study would not only contribute to the wider use of LUR studies in China but also offer important reference for the application of regular monitoring network with high representativeness in LUR studies. These results would also support for air epidemiological studies in the future.

Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China

High resolution pollution maps are critical to understand the exposure and health effect of local residents to air pollution. Currently, none of the single technologies used to measure or estimate concentrations of pollutants can provide sufficient resolved exposure data. Land use regression (LUR) models were developed to combine ground-based measurements, satellite remote sensing (SRS) and air quality model (AQM), together with geographic and local source related spatial inputs, to generate high resolution pollution maps for both PM_2.5 and NO₂ in Pearl River Delta (PRD), China. Four sets of LUR models (LUR without SRS or AQM, with SRS only, with AQM only, and with both SRS and AQM), all including local traffic emissions and land use variables, were compared to evaluate the contribution of SRS and AQM data to the performance of LUR models in PRD region. For NO₂, the annual model with SRS estimate performed best, explaining 60.5% of the spatial variation. For PM_2.5, the annual model with traditional predictor variables without SRS or AQM estimates showed the best performance, explaining 88.4% of the spatial variation. Pollution surfaces at 200 m*200 m resolution were generated according to the best performed models.

Impact of Land Use on PM2.5 Pollution in a Representative City of Middle China

Fine particulate matter (PM_2.5) pollution has become one of the greatest urban issues in China. Studies have shown that PM_2.5 pollution is strongly related to the land use pattern at the micro-scale and optimizing the land use pattern has been suggested as an approach to mitigate PM_2.5 pollution. However, there are only a few researches analyzing the effect of land use on PM_2.5 pollution. This paper employed land use regression (LUR) models and statistical analysis to explore the effect of land use on PM_2.5 pollution in urban areas. Nanchang city, China, was taken as the study area. The LUR models were used to simulate the spatial variations of PM_2.5 concentrations. Analysis of variance and multiple comparisons were employed to study the PM_2.5 concentration variances among five different types of urban functional zones. Multiple linear regression was applied to explore the PM_2.5 concentration variances among the same type of urban functional zone. The results indicate that the dominant factor affecting PM_2.5 pollution in the Nanchang urban area was the traffic conditions. Significant variances of PM_2.5 concentrations among different urban functional zones throughout the year suggest that land use types generated a significant impact on PM_2.5 concentrations and the impact did not change as the seasons changed. Land use intensity indexes including the building volume rate, building density, and green coverage rate presented an insignificant or counter-intuitive impact on PM_2.5 concentrations when studied at the spatial scale of urban functional zones. Our study demonstrates that land use can greatly affect the PM_2.5 levels. Additionally, the urban functional zone was an appropriate spatial scale to investigate the impact of land use type on PM_2.5 pollution in urban areas.

Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements

Land-use regression (LUR) models are useful for resolving fine scale spatial variations in average air pollutant concentrations across urban areas. With the rise of mobile air pollution campaigns, characterized by short-term monitoring and large spatial extents, it is important to investigate the effects of sampling protocols on the resulting LUR. In this study a mobile lab was used to repeatedly visit a large number of locations (∼1800), defined by road segments, to derive average concentrations across the city of Montreal, Canada. We hypothesize that the robustness of the LUR from these data depends upon how many independent, random times each location is visited (N_vis) and the number of locations (N_loc) used in model development and that these parameters can be optimized. By performing multiple LURs on random sets of locations, we assessed the robustness of the LUR through consistency in adjusted R² (i.e., coefficient of variation, CV) and in regression coefficients among different models. As N_loc increased, R²_adj became less variable; for N_loc = 100 vs N_loc = 300 the CV in R²_adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO_2. The CV in the R²_adj also decreased as N_vis increased from 6 to 16; from 0.090 to 0.014 for UFP. As N_loc and N_vis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.