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Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO 2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R 2 : 0.388-0.484) and had the worst direct (R 2 : 0.0001-0.349) and indirect transferability (R 2 : 0.07-0.352). Those models using the traditional method had the highest model fitting R 2 (0.629-0.966) with lower cross-validation R 2 (0.495-0.941) and slightly better direct transferability (R 2 : 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R 2 (0.601-0.966) and the best cross-validation R 2 (0.514-0.941). They also had the strongest direct transferability (R 2 : 0.006-0.590) and moderate-to-good indirect transferability (R 2 : 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The commuting microenvironment accounts for a large part of students' diurnal exposure to air pollution, especially in cities in developed countries where air pollution is caused predominantly by vehicle traffic. Accurate quantification of students' exposure and pollution dose during their commute from home to school requires their home addresses and details of the schools they attend. Such details are usually inaccessible or difficult to obtain at population scales due to privacy issues. Therefore, estimates of students' exposure to, and dose of, air pollution at population scales have to rely on simulated origins and destinations, which may bias the results. This contribution overcomes this limitation by quantifying students' terrain-based dosage of ambient nitrogen dioxide (NO 2 ) during their commute from home to school while walking along (a) the shortest-distance routes and (b) an alternative lowest-dose route. This is determined at population scales for students in Auckland, New Zealand using a rich dataset of observed home addresses and schools attended for 14,091 walking students. This study also determines the bias introduced when using simulated addresses (as opposed to observed data) to calculate the same result. Finally, we examine exposure inequalities among students of different socioeconomic backgrounds at school, at home, and during walking commutes. Results show that only 17.48% of students in the whole of Auckland can find alternative lowest-dose routes. The portion is higher (26%) in central Auckland because of its better road network connectivity. The trade-off analysis identifies that for only about 30% of students, a 1% increase in route length is associated with a >1% reduction in dosage if using the alternative lowest-dose route. Greater benefits were observed in suburban Auckland (a less-polluted area) than in central Auckland, which highlights the importance of taking an alternative lowest-dose route, especially for students whose shortest-distance routes overlap with or run parallel to an arterial road. The use of simulated addresses resulted in underestimates of both the length and reduced dosage of the alternative routes by up to a quarter in comparison with the results derived from the observed data. Limited evidence of exposure inequality based on commuter exposure was found, but patterns in the central city were opposite to those in the suburbs.

Air pollution is mostly caused by emissions from human activities, and exposure to air pollution is linked with numerous adverse human health outcomes. Recent studies have identified that although people only spend a small proportion of time on their daily commutes, the commuter microenvironment is a significant contributor to their total daily air pollution exposure. Schoolchildren are a particularly vulnerable cohort of the population, and their exposure to air pollution at home or school has been documented in a number of case studies. A few studies have identified that schoolchildren's exposure during commutes is linked with adverse cognitive outcomes and severe wheeze in asthmatic children. However, the determinants of total exposure, such as route choice and commute mode, and their subsequent health impacts on schoolchildren are still not well-understood. The aim of this paper is to review and synthesize recent studies on assessing schoolchildren's exposure to various air pollutants during the daily commute. Through reviewing 31 relevant studies published between 2004 and 2020, we tried to identify consistent patterns, trends, and underlying causal factors in the results. These studies were carried out across 10 commute modes and 12 different air pollutants. Air pollution in cities is highly heterogeneous in time and space, and commuting schoolchildren move through the urban area in complex ways. Measurements from fixed monitoring stations (FMSs), personal monitoring, and air quality modeling are the three most common approaches to determining exposure to ambient air pollutant concentrations. The time-activity diary (TAD), GPS tracker, online route collection app, and GIS-based route simulation are four widely used methods to determine schoolchildren's daily commuting routes. We found that route choices exerted a determining impact on schoolchildren's exposure. It is challenging to rank commute modes in order of exposure, as each scenario has numerous uncontrollable determinants, and there are notable research gaps. We suggest that future studies should concentrate on examining exposure patterns of schoolchildren in developing countries, exposure in the subway and trains, investigating the reliability of current simulation methods, exploring the environmental justice issue, and identifying the health impacts during commuting. It is recommended that three promising tools of smartphones, data fusion, and GIS should be widely used to overcome the challenges encountered in scaling up commuter exposure studies to population scales., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Spatial saturation studies using source-specific chemical tracers are commonly used to examine intra-urban variation in exposures and source impacts, for epidemiology and policy purposes. Most such studies, however, has been performed in North America and Europe, with substantial regional combustion-source contributions. In contrast, Auckland, New Zealand, a large western city, is relatively isolated in the south Pacific, with minimal impact from long-range combustion sources. However, fluctuating wind patterns, complex terrain, and an adjacent major port complicate pollution patterns within the central business district (CBD). We monitored multiple pollutants (fine particulate matter (PM 2.5 ), black carbon (BC), elemental composition, organic diesel tracers (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), and nitrogen dioxide (NO 2 )) at 12 sites across the ~5 km 2 CBD during autumn 2014, to capture spatial variation in traffic, diesel, and proximity to the port. PM 2.5 concentrations varied 2.5-fold and NO 2 concentrations 2.9-fold across the CBD, though constituents varied more dramatically. The highest-concentration constituent was sodium (Na), a distinct non-combustion-related tracer for sea salt (µ = 197.8 ng/m 3 (SD = 163.1 ng/m 3 )). BC, often used as a diesel-emissions tracer, varied more than five-fold across sites. Vanadium (V), higher near the ports, varied more than 40-fold across sites. Concentrations of most combustion-related constituents were higher near heavy traffic, truck, or bus activity, and near the port. Wind speed modified absolute concentrations, and wind direction modified spatial patterns in concentrations (i.e., ports impacts were more notable with winds from the northeast).

Differences in urban design features may affect emission and dispersion patterns of air pollution at local-scales within cities. However, the complexity of urban forms, interdependence of variables, and temporal and spatial variability of processes make it difficult to quantify determinants of local-scale air pollution. This paper uses a combination of dense measurements and a novel approach to land-use regression (LUR) modeling to identify key controls on concentrations of ambient nitrogen dioxide (NO2) at a local-scale within a central business district (CBD). Sixty-two locations were measured over 44 days in Auckland, New Zealand at high density (study area 0.15 km(2)). A local-scale LUR model was developed, with seven variables identified as determinants based on standard model criteria. A novel method for improving standard LUR design was developed using two independent data sets (at local and "city" scales) to generate improved accuracy in predictions and greater confidence in results. This revised multiscale LUR model identified three urban design variables (intersection, proximity to a bus stop, and street width) as having the more significant determination on local-scale air quality, and had improved adaptability between data sets.

This research assessed the comparative risk associated with exposure to traffic pollution when travelling via different transport modes in Christchurch, New Zealand. Concentrations of PM1, UFPs and CO were monitored on pre-defined routes during the morning and evening commute on people travelling concurrently by car, bus and bicycle. It was found that car drivers were consistently exposed to the highest levels of CO; on-road cyclists were exposed to higher levels of all pollutants than off-road cyclists; car and bus occupants were exposed to higher average levels of UFP than cyclists, and travellers were occasionally exposed to very high levels of pollution for short periods of time. PM10 and PM2.5 were found to be poor indicators of exposure to traffic pollution. Studying Christchurch adds to our understanding as it was a lower density city with limited traffic congestion compared most other cities previously studied., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

This paper investigates the use of the Site-Optimized Semiempirical (SOSE) air pollution model to identify the surface wind measurement site characteristics that yield the best air pollution predictions for urban locations. It compares the modelling results from twelve meteorological sites with varying anemometer heights, located at different distances from the air pollution measurements and exhibiting different land use characteristics. The results show that the index of agreement (IA) between observed and predicted concentrations can be improved from 0.4 to 0.8 by using the most compared to the least representative wind data as input to the air pollution model. Although improvements can be achieved using wind data from a site closer to the air quality monitoring site, choosing the closest wind site does not necessarily yield the best results, especially if the meteorological station is located in a region of complex land use. In addition, both the height of the anemometer and the openness of the terrain surrounding the anemometer were found to be equally important in obtaining good model predictions. The simple SOSE model can therefore be used to complement regulatory meteorological guidelines by providing a quantitative assessment of wind site representativeness for air quality applications in complex urban environments. [ABSTRACT FROM AUTHOR]

Land-use regression (LUR) modelling is widely used in many parts of the world, especially in Europe and North America. Only one published study has previously been carried out nationally in New Zealand for PM10; none for NO2. One previously identified limitation of LUR has been its limited effectiveness in small areas at fine spatial scales. In this study, LUR was used in two small areas of Auckland: Otahuhu and Mangere. Adjusted R² figures of 0.72 (Mangere) and 0.44 (Otahuhu) were comparable with other studies. The low R² found for Otahuhu may relate to the size of the monitoring area (3.5 km²), whereas the Mangere area was ten times larger (35 km²), while the extent of the modelled area was larger again: 10 km² at Otahuhu and 70 km² at Mangere. From these findings, we conclude there may be a lower limit on the size of study area used in LUR modelling, and that this area is somewhere in the range of 4-30 km². It is hypothesised that in smaller areas there is not enough variability in the local geographic features used to as accurately predict the monitored NO2 values. [ABSTRACT FROM AUTHOR]

In June 2006, two perfluorocarbon tracer experiments were conducted in central Manchester UK as part of the CityFlux campaign. The main aim was to investigate vertical dispersion in an urban area during convective conditions, but dispersion mechanisms within the street network were also studied. Paired receptors were used in most cases where one receptor was located at ground level and one at roof level. One receptor was located on the roof of Portland Tower which is an 80m high building in central Manchester. Source receptor distances in the two experiments varied between 120 and 600 m. The results reveal that maximum concentration was sometimes found at roof level rather than at ground level implying the effectiveness of convective forces on dispersion. The degree of vertical dispersion was found to be dependent on source receptor distance as well as on building height in proximity to the release site. Evidence of flow channelling in a street canyon was also found. Both a Gaussian profile and a street network model were applied and the results show that the urban topography may lead to highly effective flow channelling which therefore may be a very important dispersion mechanism should the right meteorological conditions prevail. The experimental results from this campaign have also been compared with a simple urban dispersion model that was developed during the DAPPLE framework and show good agreement with this. The results presented here are some of the first published regarding vertical dispersion. More tracer experiments are needed in order to further characterise vertical concentration profiles and their dependence on, for instance, atmospheric stability. The impact of urban topography on pollutant dispersion is important to focus on in future tracer experiments in order to improve performance of models as well as for our understanding of the relationship between air quality and public health. [ABSTRACT FROM AUTHOR]

Abstract: Space constraints in cities mean that there are only limited opportunities for increasing tree density within existing urban fabric and it is unclear whether the net effect of increased vegetation in street canyons is beneficial or detrimental to urban air quality at local scales. This paper presents data from a field study undertaken in Auckland, New Zealand designed to determine the local impact of a deciduous tree canopy on the distribution of the oxides of nitrogen within a street canyon. The results showed that the presence of leaves on the trees had a marked impact on the transport of pollutants and led to a net accumulation of pollutants in the canyon below the tree tops. The incidence and magnitude of temporally localised spikes in pollutant concentration were reduced within the tree canopy itself. A significant difference in pollutant concentrations with height was not observed when leaves were absent. Analysis of the trends in concentration associated with different wind directions showed a smaller difference between windward and leeward sides when leaves were on the trees. A small relative increase in concentrations on the leeward side was observed during leaf-on relative to leaf-off conditions as predicted by previous modelling studies. However the expected reduction in concentrations on the windward side was not observed. The results suggest that the presence of leaves on the trees reduces the upwards transport of fresh vehicle emissions, increases the storage of pollutants within the canopy space and reduces the penetration of clean air downwards from aloft. Differences observed between NO and NO2 concentrations could not be accounted for by dispersion processes alone, suggesting that there may also be some changes in the chemistry of the atmosphere associated with the presence of leaves on the trees. [Copyright &y& Elsevier]