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Air pollution poses a significant threat to human health, especially in urban areas. Urban parks function as natural biofilters, and examining the factors influencing dust retention—specifically PM2.5 and PM10 concentrations—across different spatial scales can enhance air quality and resident well-being. This study investigates the factors affecting dust retention in urban parks at both the site and vegetation community scales, focusing on Xi'an Expo Park. Through on-site measurements and a land use regression (LUR) model, the spatial and temporal distributions of PM2.5 and PM10 concentrations were analyzed. The indications of the findings are as follows. (1) The LUR model effectively predicts factors influencing PM2.5 and PM10 concentrations at the site scale, with adjusted R2 values ranging from 0.482 to 0.888 for PM2.5 and 0.505 to 0.88 for PM10. Significant correlations were found between particulate matter concentrations and factors such as the distance from factories, sampling area size, distance from main roads, presence of green spaces, and extent of hard pavements. (2) At the plant community scale, half-closed (30%–70% canopy cover), single-layered green spaces demonstrated the superior regulation of PM2.5 and PM10 concentrations. Specifically, two vegetation structures—the half-closed single-layered mixed broadleaf-conifer woodland (H1M) and the half-closed single-layered broad-leaved woodland (H1B)—exhibited the highest dust-retention capacities. (3) PM2.5 and PM10 concentrations were highest in winter, followed by spring and autumn, with the lowest levels recorded in summer. Daily particulate matter concentrations peaked between 8:00 and 10:00 a.m. and gradually decreased, reaching a minimum between 4:00 and 6:00 p.m. The objective of this study is to evaluate the impact of urban green spaces on particulate matter (PM) concentrations across multiple scales. By identifying and synthesizing key indicators at these various scales, the research aims to develop effective design strategies for urban green spaces and offer a robust theoretical framework to support the creation of healthier cities. This multi-scale perspective deepens our understanding of how urban planning and landscape architecture can play a critical role in mitigating air pollution and promoting public health. [ABSTRACT FROM AUTHOR]

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

Fine particulate matter (PM2.5) pollution affects the environment and poses threat to human health. The study of the influence of land use and other factors on PM2.5 is crucial for the rational development and utilization of territorial space. To explore the intrinsic mechanism between PM2.5 pollution and related factors, this study used the land use regression (LUR) model, and introduced geographically weighted regression (GWR), and random forest (RF) to optimize the basic LUR model. The basic LUR model was constructed to predict the annual average PM2.5 concentrations using three elements: artificial surfaces, forest land, and wind speed as explanatory variables, with adjusted R2 of 0.645. The improved LUR models based on GWR and RF, with an adjusted R2 of 0.767 and 0.821, respectively, show better fitting effects. The LUR simulation results show that the PM2.5 pollution in the northern Zhejiang is more serious and concentrated. The concentrations are also higher in regions such as the river valley plains in central Zhejiang and the coastal plains in southeastern Zhejiang. These findings show that pollution emissions should be further reduced and environmental protection should be strengthened. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared—in terms of mean square error (MSE) and mean absolute error (MAE)—with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively). [ABSTRACT FROM AUTHOR]

Introduction: The rapid growth of technology has led to an increase in air pollution in most countries of the world. One of the most serious problems that metropolitan cities such as Esfahan encounter is air pollution. The most important pollutants that should be mentioned are PM, O3, SO2, CO and NOX. The main objective of this study is to analyze the land use effects and other effective parameters such as traffic on the air quality of Esfahan and evaluating the spatial dispersion of PM, O3, SO2, CO and NOX. LUR offers an improved level of detail at which pollution variability is observed. Numerous studies have shown that land use regression (LUR) models can be applied to obtain accurate, small-scale air pollutant concentrations without a detailed pollutant emission inventory. Materials and methods: Land use regression modelling is used as a useful method for estimating changes in the concentrations of air pollutants in cities. Thus, LUR predicts the concentrations of pollution based on surrounding land use and traffic characteristics within circular areas (buffers) as predictors of measured concentrations. Moreover, the enhancement of geographic information system (GIS) techniques has contributed to the dissemination of the LUR method. Since the air pollution is in relation to factors such as population, traffic, land use, height, road length and public transportation as the most effective factors in producing these pollutants have prepared using ArcGIS 10.2 and modeled by LUR method. The regression model was run using SPSS 19. Results and discussion: With the usage of the LUR method, the most important and effective factors could be determined and modelled. It should be mentioned that among different types of land uses, residential areas and industrial regions cause the maximum effects on air pollution. Conclusion: The results of the LUR model have revealed that traffic volume, population and land use are the most important factor affected on pollutants production.

Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time.

This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

Highlights • We investigated associations of air pollution exposure with thyroid function in pregnant women. • PM 2.5 and NO 2 exposure was associated with decreased maternal free thyroxine (FT4) levels. • PM 2.5 exposure was also associated with elevated odds of maternal hypothyroxinemia. • Air pollution may interfere with maternal thyroid function during early pregnancy. Abstract Association of prenatal air pollution exposure with maternal thyroid hormone (TH) levels remains unclear, especially during early pregnancy when even small changes in maternal TH could affect fetal neurodevelopment. We examined the effect of air pollution exposure on maternal TH levels in the second trimester of pregnancy. Serum concentrations of free thyroxine (FT4) and thyroid-stimulating hormone (TSH) in 8077 pregnant women were measured by fluorescence and chemiluminescence immunoassays. Prenatal exposure to fine particulate matter (PM 2.5) and nitrogen dioxide (NO 2) was estimated using land use regression models. FT4 levels were significantly inversely associated with both PM 2.5 and NO 2 exposure. A 10 μg/m3 increase in NO 2 exposure in first trimester and PM 2.5 exposure in second trimester was associated with 0.61% (95% CI, -0.88% to -0.35%) and 0.73% (95% CI, -1.25% to -0.20%) decrease in FT4 levels, respectively. PM 2.5 exposure was also associated with elevated odds of maternal hypothyroxinemia. A 10 μg/m3 increase in PM 2.5 exposure in both first and second trimester was associated with 28% (OR = 1.28, 95% CI, 1.05–1.57) and 23% (OR = 1.23, 95% CI, 1.00–1.51) increase in the odds of maternal hypothyroxinemia, respectively. Our findings suggest that air pollution may interfere with maternal thyroid function during early pregnancy. [ABSTRACT FROM AUTHOR]

Abstract Ten relatively-low-cost ozone monitors (Aeroqual Series 500 with OZL ozone sensor) were deployed to assess the spatial and temporal variability of ambient ozone concentrations across residential areas in the Monroe County, New York from June to October 2017. The monitors were calibrated in the laboratory and then deployed to a local air quality monitoring site where they were compared to the federal equivalent method values. These correlations were used to correct the measured ozone concentrations. The values were also used to develop hourly land use regression models (LUR) based on the deletion/substitution/addition (D/S/A) algorithm that can be used to predict the spatial and temporal concentrations of ozone at any hour of a summertime day and given location in Monroe County. Adjusted R2 values were high (average 0.83) with the highest adjusted R2 for the model between 8 and 9 AM (i.e. 1–2 h after the peak of primary emissions during the morning rush hours). Spatial predictors with the highest positive effects on ozone estimates were high intensity developed areas, low and medium intensity developed areas, forests + shrubs, average elevation, Interstate + highways, and the annual average vehicular daily traffic counts. These predictors are associated with potential emissions of anthropogenic and biogenic precursors. Maps developed from the models exhibited reasonable spatial and temporal patterns, with low ozone concentrations overnight and the highest concentrations between 11 AM and 5 PM. The adjusted R2 between the model predictions and the measured values varied between 0.79 and 0.87 (mean = 0.83). The combined use of the network of low-cost monitors and LUR modeling provide useful estimates of intraurban ozone variability and exposure estimates that will be used in future epidemiological studies. Graphical abstract Unlabelled Image Highlights • Measurements with 10 low-cost sensors characterized spatial-temporal O 3 variations. • High correlations among the O 3 values were found across the metropolitan area. • Measured values were used into 24 hourly land-use regression models. • The adjusted R2 between model and measurements varied between 0.79 and 0.87. [ABSTRACT FROM AUTHOR]

Abstract Background Nitrogen dioxide (NO 2) is a typical indicator of traffic-related air pollution, and few studies with exposure assessment of high resolution have been conducted to explore its association with preterm birth in China. Objectives To investigate the association between NO 2 exposure based on a land use regression (LUR) model and preterm birth in Shanghai, China. Methods A retrospective cohort study was performed among 25,493 singleton pregnancies in a major maternity hospital in Shanghai, China, from 2014 to 2015. A temporally adjusted LUR model was used to predict the prenatal exposure to NO 2 based on residence address of each gravida. Logistic regression was performed to evaluate the associations of ambient NO 2 exposure with preterm birth during six exposure periods, including the entire pregnancy, the first trimester, the second trimester, the third trimester, the last month, and the last week before delivery. Sensitivity analysis with a matched case-control design was conducted to test the robustness of the association between NO 2 exposure and preterm birth. Results The average NO 2 concentrations during the entire pregnancy was 48.23 µg/m3 among all participants. A 10 µg/m3 increase in NO 2 concentrations was associated with preterm birth, with an adjusted odds ratio of 1.03 (95% confidence interval [CI]: 0.96,1.10) for exposures during the entire pregnancy, 1.00 (95%CI: 0.95,1.06) in the first trimester, 1.01 (95%CI: 0.96,1.07) in the second trimester, 1.07 (95%CI: 1.02,1.13) in the third trimester, 1.10 (95%CI: 1.04,1.15) and 1.05 (95%CI: 1.00,1.09) in the month and week before delivery, respectively. The results of the matched case–control analysis were generally consistent with those of main analyses. Conclusion NO 2 may increase the risk of preterm birth, especially for exposures during the third trimester, the month and the week before delivery in Shanghai, China. Graphical abstract fx1 Highlights • The first study evaluating the impacts of LUR-derived NO 2 on preterm birth in China. • The LUR model of NO 2 has a high spatial resolution and predictive ability. • The impacts of NO 2 exposure on preterm birth varied by different trimesters. • NO 2 exposure during the third trimester may increase the risk of preterm birth. [ABSTRACT FROM AUTHOR]

Abstract Accurate spatial information of PM 2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM 2.5. However, the predicted PM 2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM 2.5 in Beijing, and then seasonal PM 2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM 2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM 2.5 concentration at seasonal level (R2 ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R2 ≥ 0.81 in model fitting and testing). PM 2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM 2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing. Graphical abstract Image 1 Highlights • Detailed PM 2.5 maps from LUR and MAIAC AOD were generated. • Spatial patterns of PM 2.5 from LUR and AOD were fully compared. • Uncertainty was larger in rural and suburban area than urban. • Integrating AOD into LUR models increased model reliability. Comparing to MAIAC AOD, PM 2.5 of LUR model showed similar trend at regional scale and near traffic road but exhibited local differences. [ABSTRACT FROM AUTHOR]

Background: There are a growing number of reports on the association between air pollution and the risk of congenital anomalies. However, the results are inconsistent and most studies have only focused on the association of air pollution with congenital heart defects and orofacial clefts.Objectives: Using an exploratory study design, we aimed to identify congenital anomalies that may be sensitive to maternal exposure to specific air pollutants during the periconceptional period.Methods: We conducted a case-control study of 7426 subjects born in the 15 years between 1999 and 2014 and registered in the European Registration of Congenital Anomalies and Twins Northern Netherlands (EUROCAT NNL). Concentrations of various air pollutants (PM10, PM2.5, PM10-2.5, NO2, NOX, absorbance) were obtained using land use regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE). We linked these data to every subject in the EUROCAT NNL registry via their full postal code. Cases were classified as children or fetuses born in the 15-year period with a major congenital anomaly that was not associated with a known monogenic or chromosomal anomaly. Cases were divided into anomaly subgroups and compared with two different control groups: control group 1 comprised children or fetuses with a known monogenic or chromosomal anomaly, while control group 2 comprised all other non-monogenic and non-chromosomal registrations.Results: Using control group 1 (n = 1618) for analysis, we did not find any significant associations, but when we used control group 2 (ranges between n = 4299 and n = 5771) there were consistent positive associations between several air pollutants (NO2, PM2.5, PM10-2.5, absorbance) and the genital anomalies subgroup.Conclusion: We examined various congenital anomalies and their possible associations with a number of air pollutants in order to generate hypotheses for future research. We found that air pollution exposure was positively associated with genital anomalies, mainly driven by hypospadias. These results broaden the evidence of associations between air pollution exposure during gestation and congenital anomalies in the child. They warrant further research, which should also focus on possible underlying mechanisms. [ABSTRACT FROM AUTHOR]

The epidemiological research benefits from an accurate characterization of both spatial and temporal variability of exposure to air pollution. This work aims at proposing a method to combine the high spatial resolution of Land Use Regression (LUR) models with the high temporal resolution of fixed site monitoring data, to model spatiotemporal variability of NO 2 over a wide geographical area in Northern Italy. We developed seasonal LUR models to reconstruct the spatial distribution of a scaling factor that relates local concentrations to those measured at two reference central sites, one for the northern flat area and one for the southern mountain area. We calculated the daily average concentrations at 19 locations spread over the study areas as the product of the local scaling factor and the reference central site concentrations. We evaluated model performance comparing modeled and measured NO 2 data. LUR model's R 2 ranges from 0.76 to 0.92. The main predictors refers substantially to traffic, industrial land use, buildings volume and altitude a.s.l. The model's performance in reproducing measured concentrations was satisfactory. The temporal variability of concentrations was well captured: Spearman correlation between model and measures was > 0.7 for almost all sites. Model's average absolute errors were in the order of 10 μg m − 3 . The model for the southern area tends to overestimate measured concentrations. Our modeling framework was able to reproduce spatiotemporal differences in NO 2 concentrations. This kind of model is less data-intensive than usual regional atmospheric models and it may be very helpful to assess population exposure within studies in which individual relevant exposure occurs along periods of days or months. [ABSTRACT FROM AUTHOR]

In Bangkok, nitrogen dioxide (NO) concentrations have long been measured hourly by the Pollution Control Department (PCD) at 12 monitoring stations covering 430 km of Inner Bangkok. In the past, to estimate NO concentrations at any unmeasured location, the proximity model, interpolation model, or dispersion model was employed. These models used distance from a measured location as the sole determinant of any estimation. Toward the end of the 1990s, the more sophisticated land use regression (LUR) model was introduced. This model with its built-in geographic information system (GIS) and multiple regression analysis enabled the inclusion of other important determining variables such as land use types, traffic volume, and selected meteorological variables. This study aims to apply the LUR model for the estimation of NO concentrations over the study area covering Inner Bangkok. Monthly average NO concentrations, traffic volume, land use types, road areas together with humidity, temperature, wind speed, and rainfall data, measured at or within the vicinities of the 12 PCD stations, were input into the model. Only humidity, temperature, wind speed, rainfall, residential land use, and industrial land use were found to have influenced the NO concentrations in inner Bangkok. The resulting coefficient of determination (R squared) of 0.759 implies that 76 % of the variations in NO concentrations in inner Bangkok can be explained by the model. The study will, however, continue to obtain more precise traffic volume data in terms of time scale to improve the model. [ABSTRACT FROM AUTHOR]

BACKGROUND: Epidemiological evidence of the effects of long-term exposure to air pollution on the chronic processes of atherogenesis is limited. OBJECTIVE: We investigated the association of long-term exposure to traffic-related air pollution with subclinical atherosclerosis, measured by carotid intima media thickness (IMT) and ankle–brachial index (ABI). METHODS: We performed a cross-sectional analysis using data collected during the reexamination (2007–2010) of 2,780 participants in the REGICOR (Registre Gironí del Cor: the Gerona Heart Register) study, a population-based prospective cohort in Girona, Spain. Long-term exposure across residences was calculated as the last 10 years’ time-weighted average of residential nitrogen dioxide (NO2) estimates (based on a local-scale land-use regression model), traffic intensity in the nearest street, and traffic intensity in a 100 m buffer. Associations with IMT and ABI were estimated using linear regression and multinomial logistic regression, respectively, controlling for sex, age, smoking status, education, marital status, and several other potential confounders or intermediates. RESULTS: Exposure contrasts between the 5th and 95th percentiles for NO2 (25 μg/m3), traffic intensity in the nearest street (15,000 vehicles/day), and traffic load within 100 m (7,200,000 vehicle-m/day) were associated with differences of 0.56% (95% CI: –1.5, 2.6%), 2.32% (95% CI: 0.48, 4.17%), and 1.91% (95% CI: –0.24, 4.06) percent difference in IMT, respectively. Exposures were positively associated with an ABI of > 1.3, but not an ABI of < 0.9. Stronger associations were observed among those with a high level of education and in men ≥ 60 years of age. CONCLUSIONS: Long-term traffic-related exposures were associated with subclinical markers of atherosclerosis. Prospective studies are needed to confirm associations and further examine differences among population subgroups. [ABSTRACT FROM AUTHOR]

Abstract: The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO2) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m. A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO2 concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations. Moderate agreement was found between the URBIS and LUR in calculating NO2 concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO2. Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO2 concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better. [Copyright &y& Elsevier]

Abstract: Land use regression has been used in epidemiologic studies to estimate long-term exposure to air pollution within cities. The models are often developed toward the end of the study using recent air pollution data. Given that there may be spatially-dependent temporal trends in urban air pollution and that there is interest for epidemiologists in assessing period-specific exposures, especially early-life exposure, methods are required to extrapolate these models back in time. We present herein three new methods to back-extrapolate land use regression models. During three two-week periods in 2005–2006, we monitored nitrogen dioxide (NO2) at about 130 locations in Montreal, Quebec, and then developed a land-use regression (LUR) model. Our three extrapolation methods entailed multiplying the predicted concentrations of NO2 by the ratio of past estimates of concentrations from fixed-site monitors, such that they reflected the change in the spatial structure of NO2 from measurements at fixed-site monitors. The specific methods depended on the availability of land use and traffic-related data, and we back-extrapolated the LUR model to 10 and 20 years into the past. We then applied these estimates to residential information from subjects enrolled in a case–control study of postmenopausal breast cancer that was conducted in 1996. Observed and predicted concentrations of NO2 in Montreal decreased and were correlated in time. The estimated concentrations using the three extrapolation methods had similar distributions, except that one method yielded slightly lower values. The spatial distributions varied slightly between methods. In the analysis of the breast cancer study, the odds ratios were insensitive to the method but varied with time: for a 5ppb increase in NO2 using the 2006 LUR the odds ratio (OR) was about 1.4 and the ORs in predicted past concentrations of NO2 varied (OR∼1.2 for 1985 and OR∼1.3–1.5 for 1996). Thus, the ORs per unit exposure increased with time as the range and variance of the spatial distributions decreased, and this is due partly to the regression coefficient being approximately inversely proportional to the variance of exposure. Changing spatial variability complicates interpretation and this may have important implications for the management of risk. Further studies are needed to estimate the accuracy of the different methods. [Copyright &y& Elsevier]

Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time. [ABSTRACT FROM AUTHOR]

This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods ('rush hour') along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models. [ABSTRACT FROM AUTHOR]

Air pollution has become a global problem and can cause serious damage to human health. Epidemiological studies on the long-term exposure to air pollution can reveal the extent of this damage. Spatiotemporal land use regression (LUR) models can be used to obtain long-term pollutant concentration surfaces with high spatiotemporal resolution. However, previously established spatiotemporal LUR models generally exhibit poor spatial prediction performances in some time panels compared with their average performances. These inaccurate pollutant concentrations lead to misclassification errors in epidemiological studies. To solve this problem, a hybrid spatiotemporal LUR model system is proposed in this study, which consists of support vector regression (SVR), multiple linear regression (MLR), and a special spatiotemporal (ST) algorithm. Three SVR layers were used for the main prediction, whereas MLR and ST were used to supplement time panels with poor spatial prediction performances. In addition, temporal segmentation modeling was adopted for SVR to further improve the performance. We used the megacity Tianjin in China for our case study and six target air pollutants (CO, NO 2 , O 3 , PM 10 , PM 2.5 , and SO 2). The superiority of our model system was tested by cross-validation. The results show that the number of days on which the R2cv of the model is higher than 0.6 for CO, NO 2 , O 3 , PM 10 , PM 2.5 , and SO 2 is 363, 364, 362, 357, 360, and 362, respectively, whereas the mean of the daily R2cv on these days is 0.911, 0.903, 0.891, 0.879, 0.866, and 0.883, respectively. Based on the use of our model system, a relatively high spatial prediction performance was achieved for almost all time panels. This model system can be applied to cohort health studies to obtain the pollutant concentration surfaces of any time panel with high reliability and reduce the exposure measurement errors of misclassifications. Image 1 • Temporal segmentation models can enhance the performances of land use regression models even at small sample sizes. • The imputation for missing samples can lead to a decrease in the spatial prediction performance on some given days. • A hybrid spatiotemporal LUR model system that combines different algorithms can yield good spatial prediction performances in almost all time panels. [ABSTRACT FROM AUTHOR]

As one of the principal components of haze, fine particulate matter (PM 2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM 2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52–61% of the variation in the PM 2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM 2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM 2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution. Unlabelled Image • The land use regression (LUR) model was built using data from fixed monitoring stations in seven urban areas. • The seasonal consistency of LUR models was studied. • Spatial modelling relied on building three-dimensional morphology variables. • PM 2.5 pollution surfaces of seven urban areas were predicted using regression kriging. [ABSTRACT FROM AUTHOR]

• Influence of air quality on bikeability index was evaluated. • The proposed air quality sub-index involves cyclists' exposure to PM 2.5 and BC. • Open-source data, land-use regression models, and deep neural network were utilized. • Cyclists' exposure to TRAP is a significant component of the bikeability index. • The proposed framework is useful for recommending cycling routes in cities. Previous studies on bikeability/cycling index have explored factors that influence cycling in cities, and developed indicators to characterize a bicycle-friendly environment. However, despite its strong influence on cycling behavior, cyclists' exposure to traffic-related air pollution has been often disregarded. To close this knowledge gap, we propose a comprehensive bikeability index that comprises four sub-indices: accessibility, suitability, perceptibility, and prevailing air quality in the vicinity of cycling routes. We evaluate cyclists' exposure to fine particulate matter and black carbon, and used open-source data, land-use regression models, deep neural networks and spatial analysis. The application of the proposed bikeability framework reveals that the inclusion of air quality makes a significant difference when calculating bikeability index in Singapore and hence it merits serious consideration. We believe that the newly developed framework will convince city planners to consider the importance of assessing cyclists' exposure to airborne particles when planning cycling infrastructure. [ABSTRACT FROM AUTHOR]

With the development of urbanization, urban areas have become the main sources and sinks of polycyclic aromatic hydrocarbons (PAHs). The effects of human activities on the behaviors of PAHs in urban agglomerations have attracted significant attention. We collected soil samples (n = 330) to investigate the distribution, composition, and sources of 16 PAHs in the Yangtze River Delta Urban Agglomeration using the land resolution of 24 km × 24 km. The concentrations of Σ 16 PAHs ranged from 21 to 2034 ng/g, with a median value of 124 ± 338 ng/g. The concentrations of PAHs were highest in impervious surfaces (350 ± 352 ng/g), followed by grassland (259 ± 322 ng/g), cropland (254 ± 341 ng/g), forest (190 ± 303 ng/g), and water (68 ± 34 ng/g). PAHs were dominated by medium-molecular-weight components (4 rings PAHs), followed by PAHs with high-molecular-weight (5–6 rings PAHs) and low-molecular-weight (2–3 rings PAHs) components. Fluoranthene, benzo[ a ]anthracene and chrysene are three major pollutants in YRDUA. A positive matrix factorization model indicated that fossil fuel combustion, coal combustion and volatilization, vehicle emission, and biomass burning were the main sources of PAHs, contributing 36%, 29%, 22%, and 12% of PAH sources, respectively. Urbanization parameters were positively correlated with PAH concentrations. A land use regression (LUR) model integrated with urbanization parameters showed evidence of the strong relationship between measured PAHs and predicted PAHs. These findings together highlighted that land cover types and human activities intensively influenced the PAHs pollution in the highly urbanized zones. Unlabelled Image • Urbanization is a key factor to affect PAHs distribution in typical urban area. • Combustion sources contributed for most of PAHs in YRDUA. • Land cover types and urbanization parameters positively correlated with PAHs. • The LUR model predicted PAHs strongly correlated with measured PAHs (r2 = 0.88). [ABSTRACT FROM AUTHOR]

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. Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM 2.5), black carbon (BC) and nitrogen dioxide (NO 2) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. Two one-week integrated samples were collected at 30 PM 2.5 (BC) sites and 45 NO 2 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. LUR explained 65% of the spatial variability in PM 2.5 , 78% in BC and 73% in NO 2. Mean (±Standard Deviation) of predicted PM 2.5 , BC and NO 2 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 PM 2.5 , BC and NO 2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO 2 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 NO 2. We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM 2.5 , NO 2 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 PM 2.5 , NO 2 and BC concentrations. Image 1 • Lack of spatially resolved air pollution data limits LUR model application in China. • We are one of the few building LUR models upon specific-monitoring network in China. • PM 2.5 , BC and NO 2 models explain a large fraction of concentration variability. • We add experience on air pollution exposure assessment for population-based studies. [ABSTRACT FROM AUTHOR]

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

We aimed to establish a spatiotemporal land-use-regression (ST-LUR) model assessing individual level long-term exposure to fine particulate matters (PM 2.5 ) among 6627 adults enrolled in Guangdong province, China from 2015 to 2016. We collected weekly average PM 2.5 concentration (from the air quality monitoring stations) and visibility, population density, road density and types of land use of each air quality monitoring station and participant's residential address from April 2013 to December 2016. A ST-LUR model was established using these spatiotemporal data, and was employed to estimate the weekly average PM 2.5 concentration of each individual residential address. Data analysis was carried out by R software (version 3.5.1) and the SpatioTemporal package was used. The results showed that the ST-LUR model applying the land use data extracted using a buffer radius of 1300 m had the best modelling fitness. The results of 10-fold cross validation showed that the R 2 was 88.86% and the RMSE (Root mean square error) was 5.65 μg/m 3 . The two-year average of PM 2.5 prior to the date of investigation were calculated for each participant. This study provided a novel method to precisely assess individual level long-term exposure to ambient PM 2.5 , which may extend our understanding on the health impacts of air pollution. •Variables input in the spatiotemporal land-use-regression (ST-LUR) model include visibility, population density, road density, and types of land use.•The land use data should be extracted using a buffer radius of 1300 m.•The R 2 of the ST-LUR model was 88.86% and the RMSE was 5.65 μg/m 3 , indicating the good performance of the model., Competing Interests: The authors declare no conflict of interest., (© 2019 The Authors.)

Accurate spatial information of PM 2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM 2.5 . However, the predicted PM 2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM 2.5 in Beijing, and then seasonal PM 2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM 2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM 2.5 concentration at seasonal level (R 2  ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R 2  ≥ 0.81 in model fitting and testing). PM 2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM 2.5 . Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Black carbon (BC) is an important component of atmospheric pollution and has significant impacts on air quality and human health. Choosing Shanghai city for a case study, this paper explores the statistical characteristics and spatial patterns of BC concentrations using a mobile monitoring method, which differs from traditional fixed-site observations. Land use regression (LUR) modeling was conducted to examine the determinants for on-road BC concentrations, e.g. population, economic development, traffic, etc. These results showed that the average on-road BC concentrations were (9.86±8.68) μg·m -3 , with a significant spatial variation. BC concentrations in suburban areas[(10.47±2.04) μg·m -3 ] were 32.03% (2.54 μg·m -3 ) higher than those in the city center[(7.93±2.79) μg·m -3 ]. Besides, meteorological factors (e.g. wind speed and relative humidity) and traffic variables (e.g. the length of roads, distance to provincial roads, distance to highway) had significant effects on on-road BC concentrations ( r :0.5-0.7, P <0.01). Moreover, the LUR model, including meteorological and traffic variables performed well (adjusted R 2 :0.62-0.75, cross validation R 2 :0.54-0.69, RMSE:0.15-0.20 μg·m -3 ), which demonstrates that on-road BC concentrations in Shanghai are mainly affected by these factors and traffic sources to some extent. Among them, the most accurate LUR model was developed with a 100 m buffer, followed by the LUR model with a 5 km buffer. This study is of great significance for the identification of spatial distribution patterns for on-road BC concentration and exploring their influencing factors in Shanghai, which can provide a scientific basis and theoretical support for simulating and predicting the response mechanisms of BC on human health and the natural environment.