Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden

A Review of Urban Planning Approaches to Reduce Air Pollution Exposures

PURPOSE OF REVIEW

With only 12% of the human population living in cities meeting the air quality standards set by the WHO guidelines, there is a critical need for coordinated strategies to meet the requirements of a healthy society. One pivotal mechanism for addressing societal expectations on air pollution and human health is to employ strategic modeling within the urban planning process. This review synthesizes research to inform coordinated strategies for a healthy society. Through strategic modeling in urban planning, we seek to uncover integrated solutions that mitigate air pollution, enhance public health, and create sustainable urban environments.

RECENT FINDINGS

Successful urban planning can help reduce air pollution by optimizing city design with regard to transportation systems. As one specific example, ventilation corridors i.e. aim to introduce natural wind into urban areas to improve thermal comfort and air quality, and they can be effective if well-designed and managed. However, physical barriers such as sound walls and vegetation must be carefully selected following design criteria with significant trade-offs that must be modeled quantitatively. These tradeoffs often involve balancing effectiveness, cost, aesthetics, and environmental impact. For instance, sound walls are highly effective at reducing noise, provide immediate impact, and are long-lasting. However, they are expensive to construct, visually unappealing, and may block views and sunlight. To address the costly issue of sound walls, a potential solution is implementing vegetation with a high leaf area index or leaf area density. This alternative is also an effective method for air pollution reduction with varying land-use potential. Ultimately, emission regulations are a key aspect of all such considerations. Given the broad range of developments, concerns, and considerations spanning city management, ventilation corridors, physical barriers, and transportation planning, this review aims to summarize the effect of a range of urban planning methods on air pollution considerations.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Spatiotemporal retrieval and feature analysis of air pollution episodes

Air pollution has inevitably come along with the economic development of human society. How to balance economic growth with a sustainable environment has been a global concern. The ambient PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) is particularly life-threatening because these tiny aerosols could be inhaled into the human respiration system and cause millions of premature deaths every year. The focus of most relevant research has been placed on apportionment of pollutants and the forecast of PM2.5 concentration measures. However, the spatiotemporal variations of pollution regions and their relationships to local factors are not much contemplated in the literature. These local factors include, at least, land terrain, meteorological conditions and anthropogenic activities. In this paper, we propose an interactive analysis platform for spatiotemporal retrieval and feature analysis of air pollution episodes. A domain expert can interact with the platform by specifying the episode analysis intention considering various local factors to reach the analysis goals. The analysis platform consists of two main components. The first component offers a query-by-sketch function where the domain expert can search similar pollution episodes by sketching the spatial relationship between the pollution regions and the land objects. The second component helps the domain expert choose a retrieved episode to conduct spatiotemporal feature analysis in a time span. The integrated platform automatically searches the episodes most resembling the domain expert's original sketch and detects when and where the episode emerges and diminishes. These functions are helpful for domain experts to infer insights into how local factors result in particular pollution episodes.

Evaluating the Meteorological Effects on the Urban Form-Air Quality Relationship Using Mobile Monitoring

Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC₁₀ (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM_2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC₁₀ and 14.90% for PM_2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

Spatialization and Prediction of Seasonal NO2 Pollution Due to Climate Change in the Korean Capital Area through Land Use Regression Modeling

Urbanization is causing an increase in air pollution leading to serious health issues. However, even though the necessity of its regulation is acknowledged, there are relatively few monitoring sites in the capital metropolitan city of the Republic of Korea. Furthermore, a significant relationship between air pollution and climate variables is expected, thus the prediction of air pollution under climate change should be carefully attended. This study aims to predict and spatialize present and future NO₂ distribution by using existing monitoring sites to overcome deficiency in monitoring. Prediction was conducted through seasonal Land use regression modeling using variables correlated with NO₂ concentration. Variables were selected through two correlation analyses and future pollution was predicted under HadGEM-AO RCP scenarios 4.5 and 8.5. Our results showed a relatively high NO₂ concentration in winter in both present and future predictions, resulting from elevated use of fossil fuels in boilers, and also showed increments of NO₂ pollution due to climate change. The results of this study could strengthen existing air pollution management strategies and mitigation measures for planning concerning future climate change, supporting proper management and control of air pollution.

Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data

Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM_2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM_2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China

The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models

Previous studies have characterized spatial patterns of pollution with land use regression (LUR) models from distributed passive or filter samplers at low temporal resolution. Large-scale deployment of low-cost sensors (LCS), which typically sample in real time, may enable time-resolved or real-time modeling of concentration surfaces. The aim of this study was to develop spatiotemporal models of PM_2.5, NO₂, and CO using an LCS network in Pittsburgh, Pennsylvania. We modeled daily average concentrations in August 2016-December 2017 across 50 sites. Land use variables included 13 time-independent (e.g., elevation) and time-dependent (e.g., temperature) predictors. We examined two models: LUR and a machine-learning-enabled land use model (land use random forest, LURF). The LURF models outperformed LUR models, with increase in the average externally cross-validated R² of 0.10-0.19. Using wavelet decomposition to separate short-lived events from the regional background, we also created time-decomposed LUR and LURF models. Compared to the standard model, this resulted in improvement in R² of up to 0.14. The time-decomposed models were more influenced by spatial parameters. Mapping our models across Allegheny County, we observed that time-decomposed LURF models created robust PM_2.5 predictions, suggesting that this approach may improve our ability to map air pollutants at high spatiotemporal resolution.

On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes

Air pollution assessment in the Tropical Andes requires a multidisciplinary approach. This can be supported from the understanding of the underlying biological dynamics and atmospheric behavior, to the mathematical approach for the proper use of all available information. This review paper touches on several aspects in which mathematical models can help to solve challenging problems regarding air pollution in reviewing the state-of-the-art at the global level and assessing the corresponding state of development as applied to the Tropical Andes. We address the complexities and challenges that modelling atmospheric dynamics in a mega-diverse region with abrupt topography entails. Understanding the relevance of monitoring and facing the problems of data scarcity, we call attention to the usefulness of data assimilation for uncertainty reduction, and how these techniques could help tackle the scarcity of regional monitoring networks to accelerate the implementation and development of modelling systems for air quality in the Tropical Andes. Finally, we suggest a cyberphysical framework for decision-making processes based on the data assimilation of chemical transport models, the forecast of scenarios, and their use in regulation and policy making.

Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

Spatial Analysis of Built Environment Risk for Respiratory Health and Its Implication for Urban Planning: A Case Study of Shanghai

Urban planning has been proven and is expected to promote public health by improving the built environment. With a focus on respiratory health, this paper explores the impact of the built environment on the incidence of lung cancer and its planning implications. While the occurrence of lung cancer is a complicated and cumulative process, it would be valuable to discover the potential risks of the built environment. Based on the data of 52,009 lung cancer cases in Shanghai, China from 2009 to 2013, this paper adopts spatial analytical methods to unravel the spatial distribution of lung cancer cases. With the assistance of geographic information system and Geo-Detector, this paper identifies certain built environments that are correlated with the distribution pattern of lung cancer cases in Shanghai, including the percentage of industrial land (which explains 28% of the cases), location factors (11%), and the percentages of cultivated land and green space (6% and 5%, respectively). Based on the quantitative study, this paper facilitates additional consideration and planning intervention measures for respiratory health such as green buffering. It is an ecological study to illustrate correlation that provides approaches for further study to unravel the causality of disease incidence and the built environment.

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

Coastal cities have been experiencing tremendous land use changes worldwide. Studies on the consequences of land use change in coastal cities have provided helpful information for spatial regulations and have attracted increased attention. Changes in forests and water bodies, however, have rarely been investigated, challenging the formation of a holistic pattern of land use change. In this study, we selected Ningbo, China, as a case study area and analyzed its land use change from 1990 to 2016. Random forest (RF) classification was employed to derive land use information from Landsat images. Transition matrices and a distribution index (DI) were applied to identify the major types of land use transitions and their spatial variations by site-specific attributes. The results showed that the entire time period could be divided into two stages, based on the manifestations of land use change in Ningbo: 1990–2005 and 2005–2016. During 1990–2005, construction land expanded rapidly, mainly through the occupation of agricultural land and forest, while during 2005–2016, the main change trajectory turned out to be a small net change in construction land and a net increase in agricultural land sourced from construction land, forests, and water bodies. In terms of land use change by site-specific attributes, the rapid expansion of construction land around the municipal city center during 1990–2005 was restrained, and similar amounts of land conversion between construction and agricultural use occurred during 2005–2016. During the study period, areas undergoing land use change also showed trends of moving outward from the municipal city center and the county centers located adjacent to roads and the coastline and of moving up to hilly areas with steeper slopes and higher elevations. Protecting reclaimed agricultural land, improving the efficiency of construction land, and controlling forest conversion in hilly areas are suggested as spatial regulations in Ningbo city.

Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R²) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

Remote Sensing in Environmental Justice Research—A Review

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.

Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

Air pollution has a major impact on human health and quality of life; therefore, its determinants should be studied to promote effective management and reduction. Here, we examined the influence of the built environment on air pollution by analyzing the relationship between the built environment and particulate matter (i.e., PM2.5 and PM10). Air pollution data collected in Seoul in 2014 were spatially mapped using geographic information system tools, and PM2.5 and PM10 concentrations were determined in individual neighborhoods using an interpolation method. PM2.5 and PM10 failed to show spatial autocorrelation; therefore, we analyzed the associations between PM fractions and built environment characteristics using an ordinary least squares regression model. PM2.5 and PM10 exhibited some differences in spatial distributions, suggesting that the built environment has different effects on these fractions. For instance, high PM10 concentrations were associated with neighborhoods with more bus routes, bus stops, and river areas. Meanwhile, both PM2.5 and PM10 were more likely to be high in areas with more commercial areas and multi-family housing, but low in areas with more main roads, more single-family housing, and high average gross commercial floor area. This study is expected to contribute to establishing policies and strategies to promote sustainability in Seoul, Korea.

Modeling Wildfire Smoke Pollution by Integrating Land Use Regression and Remote Sensing Data: Regional Multi-Temporal Estimates for Public Health and Exposure Models

To understand the health effects of wildfire smoke, it is important to accurately assess smoke exposure over space and time. Particulate matter (PM) is a predominant pollutant in wildfire smoke. In this study, we develop land-use regression (LUR) models to investigate the impact that a cluster of wildfires in the northwest USA had on the level of PM in southern Alberta (Canada), in the summer of 2015. Univariate aerosol optical depth (AOD) and multivariate AOD-LUR models were used to estimate the level of PM2.5 in urban and rural areas. For epidemiological studies, it is also important to distinguish between wildfire-related PM2.5 and PM2.5 originating from other sources. We therefore subdivided the study period into three sub-periods: (1) Pre-fire, (2) during-fire, and (3) post-fire. We then developed separate models for each sub-period. With this approach, we were able to identify different predictors significantly associated with smoke-related PM2.5 verses PM2.5 of different origin. Leave-one-out cross-validation (LOOCV) was used to evaluate the models’ performance. Our results indicate that model predictors and model performance are highly related to the level of PM2.5, and the pollution source. The predictive ability of both uni- and multi-variate models were higher in the during-fire period than in the pre- and post-fire periods.