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This paper aims to foster an explicit geoethical orientation in physical geography. Using examples from Aotearoa New Zealand, we approach the work of physical geography with a set of ethical coordinates derived from our research, arguing that they allow for greater sensitivity in considering what is more-than-human in our research relationships. Working with these ethical co-ordinates lays the political groundwork for thinking and doing physical geography differently in the pursuit of less exploitative social and ecological relations. Our proposition offers new potentials for the practice of geography more generally: opportunities for enactive research encounters, those that perform generative change for a decolonised, post-productivist, physical geography.

“Wicked problems” are complex to understand and challenging to teach. Our experience of teaching about environmental concerns in Aotearoa New Zealand suggests how these concepts are taught is more ...

Accurate measurements of the depth of the convective boundary layer (CBL) are fundamental for understanding and forecasting weather, air quality, and climate. However, the CBL depth (BLD) shows significant spatial and temporal variability, which is challenging to measure and model. Ceilometer instruments, which estimate the CBL depth from aerosol layers, are relatively cheap, have high temporal resolution, and have the potential for increased spatial coverage. Nevertheless, their performance in subtropical environments with low aerosol concentrations is unknown. Furthermore, climatological studies of the variability in the urban CBL depth are scarce, especially in the Southern Hemisphere, and very few studies have examined the relationship between BLD variability and synoptic conditions. Using three years of continuous ceilometer data, we present a novel climatology of the temporal variability in CBL depth over the Southern Hemisphere subtropical, coastal city of Auckland, New Zealand. The results indicate that the median daytime maximum CBL depth (BLDMAX) over the Auckland CBD is 1100 m a.g.l. (above ground level) during summer and 700 m a.g.l. during winter. Strong relationships are found between the maximum CBL depth and both solar radiation and soil moisture; however, no relationship is found between the maximum CBL depth and the synoptic conditions as determined by the New Zealand Kidson weather types synoptic classification. The absence of a relationship between the maximum CBL depth and Kidson weather types may explain the poor correlation previously observed between synoptic class and air pollution events in Auckland.

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 (NO2) 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.

Erionite is a fibrous zeolite found in the rock beneath parts of Auckland, New Zealand. As a known carcinogen that can be easily inhaled. it has been linked to the development of mesothelioma. It has a high degree of carcinogenicity due to the specific physical dimensions of the fibres, with certain sizes able to easily deposit deep in the lung tissue once inhaled. This project seeks to understand the extent to which erionite, once exposed, propagates in the ambient air in the form of dust and airborne fibres. The research is made challenging by the lack of standard methodological approaches to detect and quantify such fibres in airborne samples. Initial monitoring has been targeted on areas that have the combined attributes of rapid urban development (and thus excavation) and a confirmed presence of erionite in the rock strata beneath. The key sampling periods will be during the New Zealand summertime when warm temperatures and drier conditions prevail, increasing the potential for dust and fibre generation and buoyancy. The research findings from this project aim to develop a standard method for investigating erionite fibres in ambient air, including recommendations for the use and application of suitable screening methods, instrumentation and field site choice and instrument network density for given scenarios.

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

Standard Land Use Regression (LUR) models rely on one universal equation for the entire city or study area. Since this approach cannot represent the heterogeneous controls on pollutant dispersion in central, urban and suburban areas effectively the models are not transferable. Further, if different land use types are not adequately sampled in the measurement campaign, model estimates of local-scale pollutant concentrations may be poor. In this study, this deficiency is overcome with a site-optimised multi-scale GIS based LUR modelling approach developed. This approach is used to simulate nitrogen dioxide (NO2) concentrations in Auckland at three scales (central business district (CBD), urban, and suburban). The simulated NO2 distribution clearly shows a higher concentration of pollution along arterial roads and motorways as expected. Areas of limited dispersion (such as among high-rise buildings of the CBD) are also identified as high pollution areas. Predictor variables vary between scales; no single variable is common to all the scales. The leave-one-out cross validation (LOOCV) revealed that the multi-scale LUR model achieved an R2 of 0.62, 0.86 and 0.73, respectively, at the CBD, urban, and suburban scales. The corresponding LOOCV root-mean-square-errors (RMSE) were 5.58, 3.53 and 4.41 μg·m−3 respectively. Based on these statistical measures the multi-scale LUR model performs slightly better than the universal kriging (UK) model and the standard LUR model, and significantly better than the inverse distance weighting (IDW) and ordinary kriging (OK) models. When evaluated against external observations at eight fixed regulatory monitoring stations, the multi-scale LUR model out-performed all four of the other models considered and achieved an R2 value of 0.85 with the lowest RMSE (8.48 μg·m−3). This approach offers a robust alternative for modelling and mapping spatial concentrations of NO2 pollutants at multi-scales in large study areas with distinct urban design and configurations.

The strong temporal and spatial gradients in NO 2 concentrations frequently observed in urban microenvironments are very difficult to measure and model accurately. Recent developments in low-cost air quality instruments have led to improvement in the spatial coverage of time-resolved measurement, however interpolation is still needed to map pollutant concentrations and connect time-as well as space-dependent variations to urban design features. Here we propose a novel approach that uses a previously-described microscale land use regression (LUR) model to spatially interpolate data from a well-calibrated network of low-cost air quality instruments. We use a semiconducting oxide-based ozone sensor to provide a robust correction of the output of an electrochemical NO 2 sensor for ozone interference. We characterise signal noise probably associated with meniscus fluctuations as a significant error source, that can be handled with appropriate signal averaging. The LUR model is used to provide high spatial resolution in the data set, whilst correlation with sensor measurements provides a time-dependent estimate associated with different land use types. Observations from the network of instruments showed marked variability in NO 2 concentrations over short distances (on the scale of 100 m), with highest concentrations reached near bus stops, intersections and under shop awnings. This approach connects the complex time- and space-dependent variations to urban design features and is a promising way forward as a basis for objective spatial mapping of time-dependent mean concentration fields and local population exposure estimates.

Children walking to school are at a high risk of exposure to air pollution compared with other modes because of the time they spend in close proximity to traffic during their commute. The aim of this study is to investigate the effect of a walker’s route choice on their exposure to ultrafine particles (UFP) on the walk to school. During morning commutes over a period of three weeks, exposure to UFP was measured along three routes: two routes were alongside both sides of a busy arterial road with significantly higher levels of traffic on one side compared to the other, and the third route passed through quiet streets (the background route). The results indicate that the mean exposure for the pedestrian walking along the background route was half the exposure experienced on the other two routes. Walkers on the trafficked side were exposed to elevated concentrations (&gt, 100,000 pt/cc) 2.5 times longer than the low-trafficked side. However, the duration of the elevated exposure for the background route was close to zero. Public health officials and urban planners may use the results of this study to promote healthier walking routes to schools, especially those planned as part of organized commutes.

A brown air pollution haze that forms over some international cities during the winter has been found to be associated with negative health outcomes and high surface air pollution levels. Previous research has demonstrated a well-established link between the structure of the atmospheric boundary layer (ABL) and surface air quality; however, the degree to which the structure of the ABL influences for formation of local-scale brown haze is unknown. Using continuous ceilometer data covering seven consecutive winters, we investigate the influence of the structure of the ABL in relation to surface air pollution and brown haze formation over an urban area of complex coastal terrain in the Southern Hemisphere city of Auckland, New Zealand. Our results suggest the depth and evolution of the ABL has a strong influence on severe brown haze formation. When days with severe brown haze are compared with those when brown haze is expected but not observed (based on favorable meteorology and high surface air pollution levels), days with severe brown haze are found to coincide with significantly shallower daytime convective boundary layers (~ 48% lower), and the nights preceding brown haze formation are found to have significantly shallower nocturnal boundary layers (~ 28% lower). On severe brown haze days the growth rate during the morning transition phase from a nocturnal boundary layer to a convective daytime boundary layer is found to be significantly reduced (70 m h-1) compared to days on which brown haze is expected but not observed (170 m h-1). Compared with moderate brown haze, severe brown haze conditions are found to be associated with a significantly higher proportion of days with a distinct residual layer present in the ceilometer profiles, suggesting the entrainment of residual layer pollutants may contribute to the severity of the haze. This study illustrates the complex interaction between the ABL structure, air pollution, and the presence of brown haze, and demonstrates the utility of a ceilometer instrument in understanding and predicting the occurrence of brown haze events.

As air pollution varies significantly in both space and time, commuter exposure may differ considerably depending on the route taken between home to work. This is especially the case for active mode commuters who often have a wider range of route choices available to them compared with those traveling by private motor vehicle or by public transport. The aim of this study was to investigate the effect of route choice on air pollution exposure among active commuters, and to estimate, based on modeling, the health benefits able to be achieved from air pollution exposure reductions, modeled across a population, through route optimization. We searched for studies that used portable personal air pollution monitoring equipment during active mode commuting, and reported measurements of air quality on at least two routes, either as a journey to work or to school. The World Health Organization (WHO) model AirQ+ was then exploited to estimate the premature deaths attributable to air pollution according to route choice. Ten publications were identified that met the inclusion criteria. Ultrafine particle counts (UFP), black carbon (BC), and carbon monoxide (CO) were the most commonly measured pollutants in the studies identified. The exposures associated with “high exposure” and “low exposure” routes (categorized based on differences in traffic counts on the roads along the commute route or walking on opposite sides of the road with different levels of traffic traveling in each direction) were found to vary on average by 30 ± 8%, 42 ± 35%, and 55 ± 17% for BC, CO and UFP, respectively. On the basis of modeling, and on the estimated exposures to BC, up to 36 out of 10,000 deaths could be prevented by choosing a low exposure route compared with a high exposure route during active commuting. The results of this study may be useful for both individuals in their commute planning, and also for urban transport planners as impetus for investing in infrastructure to support healthy active mode commuting.

The aim of the UN Sustainable Development Goals (SDGs) is to achieve a better and more sustainable future for all by 2030. Since the majority of the global population lives in cities, it is crucial to identify, evaluate and implement urban interventions (such as zero carbon housing, active transport, better urban connectivity, air pollution control, clean household fuels, and protection fromheat and flood events) that will improve health andwellbeing and make our natural and built environment more sustainable. This Virtual Special Issue (VSI) comprises of 14 diverse case studies, methods and tools that provide suggestions for interventions which directly or indirectly support the achievement of the UN SDGs. (c) 2020 Published by Elsevier B.V.

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

Overseas, emerging research has shown that where erionite is present in bedrock as a zeolite, and then subsequently disturbed and blown into the atmosphere, resulting exposure is associated with health effects similar to those caused by asbestos, including malignant mesothelioma (MM). Erionite-induced MM is thought to be particularly prevalent in the construction and quarrying industries, in regions where rock containing erionite is disturbed. In 2015, the then Government Chief Scientist, Sir Peter Gluckman, reported that erionite was a more potent carcinogen than asbestos, and more recent studies have established its presence in the Auckland Region. However, globally at present, there are no established occupational exposure limits for erionite, standard sampling and analytical methods or exposure mitigation guidelines. Given the many major construction projects being carried out in Auckland at the present time, which involve the removal of large quantities of bedrock containing erionite, an assessment of the health risks such activities pose to the public is needed.

Timber treated with the anti-fungal chemical copper chrome arsenate is used extensively in the New Zealand building industry. While illegal, the burning of treated timber is commonplace in New Zealand and presents a health risk. Outdoor ambient monitoring of arsenic in airborne particulate matter in New Zealand has identified levels that exceed the maximum standards of 5.5 ng m&minus, 3 (annual average) at some urban locations. In this study, two-week-old beard hair samples were collected during the winter months to establish individual exposure to arsenic using Inductively Coupled Plasma-Mass Spectrometry. These results were then compared with questionnaire data about wood burner use for the two weeks prior to sampling, and spatial trends in arsenic from ambient monitoring. Results suggest that the burning of construction timber that may contain arsenic is associated with a higher level of arsenic in hair than those who burn logs or coal exclusively. There is no association between the area-level density of wood burners and arsenic levels but a significant correlation with individual household choice of fuel as well as the smell of wood smoke in the community, suggesting very localised influences. Strategies are needed to raise awareness of the risks of burning treated timber and to provide economically-viable alternatives.

Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.

Land use regression (LUR) analysis has become a key method to explain air pollutant concentrations at unmeasured sites at city or country scales, but little is known about the applicability of LUR at microscales. We present a microscale LUR model developed for a heavy trafficked section of road in Auckland, New Zealand. We also test the within-city transferability of LUR models developed at different spatial scales (local scale and city scale). Nitrogen dioxide (NO2) was measured during summer at 40 sites and a LUR model was developed based on standard criteria. The results showed that LUR models are able to capture the microscale variability with the model explaining 66% of the variability in NO2 concentrations. Predictor variables identified at this scale were street width, distance to major road, presence of awnings and number of bus stops, with the latter three also being important determinants at the local scale. This highlights the importance of street and building configurations for individual exposure at the street level. However, within-city transferability was limited with the number of bus stops being the only significant predictor variable at all spatial scales and locations tested, indicating the strong influence of diesel emissions related to bus traffic. These findings show that air quality monitoring is necessary at a high spatial density within cities in capturing small-scale variability in NO2 concentrations at the street level and assessing individual exposure to traffic related air pollutants.

Recent improvements in low-cost air quality instrumentation make deployment of dense networks of sensors possible. However, the shear volume of data from these networks means that traditional methods for data quality control and data analysis are no longer viable. We propose a real-time data scanning routine that detects local and regional variability within the data sets. This can be used to differentiate errors resulting from instrument malfunction or calibration drifts from natural (environmentally driven) regional changes in ambient concentrations. Our case study considered hourly-averaged ozone data from Texas and from two networks in Vancouver. We used 7 and 28 days of data for the algorithm initialisation with simulated and real instrumental changes. The algorithm output can be used as part of a limited resource maintenance schedule for sensor networks, and to improve understanding of air quality processes and their relation to environmental and public health data.

Summary Auckland is New Zealand’s fastest growing region, with large infrastructure projects that often include, and have included, tunnelling. The Waterview Connection saw twin tunnels driven through weathered and unweathered Waitemata Group rocks, generating c. 800,000 m3 of spoil. The material was excavated from tunnels, and transferred via conveyor, before being trucked to Wiri Quarry. The City Rail Link (CRL) and Central Interceptor excavations will also generate significant volumes of spoil., some of which is believed to contain erionite. Erionite is a naturally occurring “asbestiform” mineral that belongs to a group of silicates called zeolites. It is listed by the International Agency for Research on Cancer (part of the World Health Organization) as a Group 1 known carcinogen. Studies have demonstrated that erionite is more carcinogenic than asbestos, with similar effects to asbestos, such as malignant mesothelioma (MM). In Auckland, rapid infrastructure and residential development creates a wide number of open excavations in the city, suburbs and region, leaving erionite-bearing Waitemata Group and Waitakere Group rocks exposed. Thus, there is the potential for the exposure of Auckland’s population to erionite-bearing dust, not just in occupational settings, but also in residential and central urban areas, where the material is disturbed, transported, and disposed of.

We demonstrate how a hierarchical network comprising a number of compliant reference stations and a much larger number of low-cost sensors can deliver reliable high temporal-resolution ozone data at neighbourhood scales. The framework, demonstrated originally for a smaller scale regional network deployed in the Lower Fraser Valley, BC was tested and refined using two much more extensive networks of gas-sensitive semiconductor-based (GSS) sensors deployed at neighbourhood scales in Los Angeles: one of ~20 and one of ~45 GSS ozone sensors. Of these, ten sensors were co-located with different regulatory measurement stations, allowing a rigorous test of the accuracy of the algorithms used for off-site calibration and adjustment of low cost sensors. The method is based on adjusting the gain and offset of the low-cost sensor to match the first two moments of the probability distribution of the sensor result to that of a proxy: a calibrated independent measurement (usually derived from regulatory monitors) whose probability distribution evaluated over a time that emphasizes diurnal variations is similar to that at the test location. The regulatory measurement station physically closest to the low-cost sensor was a good proxy for most sites. The algorithms developed were successful in detecting and correcting sensor drift, and in identifying locations where geographical features resulted in significantly different patterns of ozone variation due to the relative dominance of different dispersion, emission and chemical processes. The entire network results show very large variations in ozone concentration that take place on short time- and distance scales across the Los-Angeles region. Such patterns were not captured by the more sparsely distributed stations of the existing regulatory network and demonstrate the need for reliable data from dense networks of monitors., 28 pages, 12 figures, Supplementray information appended has 14 pages

Brown haze, observed over some cities during the winter months, has been found to be associated with poor surface air quality and negative health outcomes. While the link between the atmospheric boundary layer (ABL) structure and surface air quality is well-established, the degree to which boundary layer structure influences local brown haze formation is unknown. Using continuous ceilometer data from seven winters, we investigate the influence of the ABL structure in relation to surface air pollution and brown haze formation over an urban area of complex coastal terrain in the Southern Hemisphere city of Auckland, New Zealand. When days with severe brown haze are compared with those when brown haze is expected but not observed (based on favorable meteorology and high surface air pollution levels), the presence of severe brown haze is found to coincide with a significantly shallower boundary layer during both the daytime convective phase (~48% lower) and the early-morning nocturnal phase (~28% lower). Severe brown haze is also found to be associated with significantly reduced morning transition growth rates (70 m h−1) compared to days on which brown haze is expected but not observed (170 m h−1). Our results also suggest the entrainment of pollutants stored in the residual layer may contribute to the severity of the haze. This study illustrates the complex interactions between the ABL structure, air pollution, and the presence of brown haze, and demonstrates the utility of a ceilometer instrument in understanding and predicting the occurrence of brown haze events.

The big data revolution is changing the way data is produced, analyzed, and valued. In the environmental sciences, big data has made it onto the agenda through calls to utilize the current data “deluge” more effectively and a desire for more complete measurement. However, a wider philosophical and ethical critique of big data is needed to assess its utility for environmental explanation. We distil three definitions relevant to the environmental sciences, focusing on the characteristics that make data “big,” the methods of analysis used, and the models of explanation favoured by big data analysts. We critically interrogate the new priorities implicit within big environmental data, and for a historical analogue we compare the big data moment in the environmental sciences to the period in the 1970s when systems theory was being invoked as a paradigmatic shift. Like systems theory, big data is poised to become the new lingua franca of many fields of scientific inquiry. Here we echo Barbara Kennedy's caution that whilst new methods of analysis seem fascinating and promissory, scientists must always be accountable to the “naughty” world in which we live, rather than the clean abstractions that we seek to generate. Les megadonnees peuvent-elles apprivoiser un monde « refractaire » ? La revolution des megadonnees est en voie de modifier la maniere dont on produit, analyse et valorise le savoir. Les sciences de l'environnement accordent aux megadonnees une attention prioritaire par la promotion d'un emploi plus efficace du « deluge » de donnees et le souhait de se doter d'une mesure plus exhaustive. C'est toutefois sur la base d'une critique philosophique et ethique elargie des megadonnees que leur utilite en matiere environnementale peut etre etablie. Trois definitions pertinentes pour les sciences de l'environnement sont elaborees a partir des elements qui distinguent les megadonnees, les methodes d'analyse utilisees et les modeles d'explication privilegies par les chercheurs qui se servent des megadonnees. Nous examinons d'un œil critique les nouvelles priorites issues des megadonnees environnementales et nous comparons, en etablissant un parallele historique, l'epoque des megadonnees dans les sciences de l'environnement a celle des annees 1970 alors qu'on alleguait que la theorie des systemes entrainait un changement paradigmatique. A l'instar de la theorie des systemes, les megadonnees sont sur le point de devenir la nouvelle langue vehiculaire de nombreux domaines d'activites scientifiques. Nous reprenons ici l'avertissement emis par Barbara Kennedy qui soulignait que bien que les nouvelles methodes d'analyse puissent paraitre fascinantes et prometteuses, cela ne dispense pas les scientifiques de toujours rendre compte du monde « refractaire » dans lequel nous vivons, plutot que des abstractions pures auxquelles nous aspirons.

Ozone (O 3 ) concentrations in urban areas are spatially and temporally variable, influenced by chemical production, depletion through deposition and chemical titration processes and dispersion. To date, analysis of intra-urban variability of O 3 concentrations, and the influence of local controls on production and depletion rates, has been limited due to the low spatial and/or temporal resolution of measurements. We demonstrate that measurements made using a carefully managed multi-sensor network of low-cost gas-sensitive semiconductor instruments are sufficiently precise to resolve subtle but significant variations in ozone concentration across a region. Ozone was measured at 12 sites in the isolated subtropical city of Auckland, New Zealand. Overall O 3 concentrations in the Auckland region were low (annual mean: 19 ppb) across all seasons, with a minimum in summer. Higher O 3 concentrations (max. 57 ppb) were observed when wind speeds were > 5 m s − 1 and from the W/SW, and were associated with maritime air masses. Ozone formation in the Auckland region is low, which is attributed to a combination of the low O 3 background concentrations, the negligible contribution of long-range transport and the effect of NOx titration. Intra-urban variability showed that the lowest O 3 concentrations were measured at the residential sites, particularly at night and during rush hours. Ozone depletion from reaction with traffic-generated NO explains the rush-hour minima but did not fully account for the low night-time values. The results suggest that night-time depletion may result from other processes such as the reaction of ozone with nitrite on surfaces such as concrete, pointing towards the need for further studies concerning the rate and mechanism of dry deposition at night in urban areas.

Urban tree planting programmes are increasingly promoted as a way to reduce atmospheric carbon dioxide (CO2) mixing ratios. However, few studies have investigated the photosynthetic CO2 uptake potential of different urban tree species across seasons. In particular little is known about photosynthetic CO2 uptake in cities with a subtropical, oceanic climate where evergreen species are dominant. We addressed this shortcoming by measuring net photosynthetic rates of ten native and exotic tree species during different seasons and times of the day in Auckland, New Zealand. We also assessed the potential of leaf nitrogen (N) concentration as a proxy for net photosynthetic capacities of urban trees, which is of particular importance to upscale leaf-level photosynthetic CO2 uptake to local and regional scales. In addition, we compared measured net photosynthetic capacities (light-saturated net photosynthetic rates) with carbon (C) sequestration rates estimated using tree growth measurements and allometric equations. Mean net photosynthetic capacities ranged between 2.37 and 10.48 μmol m−2 s−1 across all seasons and were closely related to tree C sequestration rates, suggesting that increased photosynthesis enhances growth rates and therefore tree C sequestration rates. Given that winter net photosynthetic capacities remained high in evergreen species (3.38–13.96 μmol m−2 s−1), with almost 50% higher mean net photosynthetic capacity compared to summer across all species, we suggest that tree planting programmes for CO2 mitigation should favour long living evergreen tree species with high basal area increments (BAI). Leaf N explained 43% and 57% of the variability of photosynthetic capacities across species in summer and winter, respectively. These results indicate that leaf N may be used as a proxy for net photosynthetic capacities of commonly planted urban trees in Auckland. However, further research is required to provide robust models that may be used to estimate photosynthetic CO2 uptake at a local and urban scale.

Measurements of CO2 fluxes in temperate climates have shown that urban areas are a net source of CO2 and that photosynthetic CO2 uptake is generally not sufficient to offset local CO2 emissions. However, little is known about the role of vegetation in cities where biogenic CO2 uptake is not limited to a 2–8 months growing season. This study used the eddy covariance technique to quantify the atmospheric CO2 fluxes over a period of 12 months in a residential area in subtropical Auckland, New Zealand, where the vegetation cover (surface cover fraction: 47%) is dominated by evergreen vegetation. Radiocarbon isotope measurements of CO2 were conducted at three different times of the day (06:00–09:00, 12:00–15:00, 01:00–04:00) for four consecutive weekdays in summer and winter to differentiate anthropogenic sources of CO2 (fossil fuel combustion) from biogenic sources (ecosystem respiration, combustion of biofuel/biomass). The results reveal previously unreported patterns for CO2 fluxes, with no seasonal variability and negative (net uptake) CO2 midday fluxes throughout the year, demonstrating photosynthetic uptake by the evergreen vegetation all year-round. The winter radiocarbon measurements showed that 85% of the CO2 during the morning rush hour was attributed to fossil fuel emissions, when wind was from residential areas. However, for all other time periods radiocarbon measurements showed that fossil fuel combustion was not a large source of CO2, suggesting that biogenic processes likely dominate CO2 fluxes at this residential site. Overall, our findings highlight the importance of vegetation in residential areas to mitigate local CO2 emissions, particularly in cities with a climate that allows evergreen vegetation to maintain high photosynthetic rates over winter. As urban areas grow, urban planners need to consider the role of urban greenspace to mitigate urban CO2 emissions.

Urban soils are an important soil organic carbon (SOC) store, but soils are also a natural source of carbon dioxide (CO2) due to soil CO2 efflux derived from decomposition and root related respiration. Little is known about the temporal and spatial variability of SOC stocks and soil CO2 efflux in urban areas, in particular in the southern hemisphere. The objectives of our study were to quantify SOC stocks and soil CO2 efflux in tree (urban forests) and grass dominated (parklands) urban ecosystems and to investigate the underlying processes which determine urban SOC stocks and soil CO2 efflux. Soil organic C stocks were quantified to 30 cm depth and soil CO2 efflux was determined using a closed chamber technique at 16 sites across Auckland, New Zealand. Vegetation and soil characteristics were measured to 30 cm depth at each study plot. In addition, the diurnal and seasonal variability in soil CO2 efflux was assessed at six sites. Median soil organic C stocks (0–10 cm) were significantly higher in parkland soils (4.8 kg m− 2, IQR: 0.9) compared to urban forest soils (2.7 kg m− 2, IQR: 0.1), which was largely due to higher bulk densities in parkland soils. In contrast, median soil CO2 efflux was not significantly different between parklands (5.2 μmol m− 2 s− 1, IQR: 0.8) and urban forests (4.5 μmol m− 2 s− 1, IQR: 1.9) sites. This suggests that increasing soil bulk densities without restricting root growth may enhance SOC stocks without increasing soil CO2 efflux. Soil temperature and soil water content explained 54% (urban parkland) and 71% (urban forests) of the temporal variation in soil CO2 efflux. Urban SOC stocks and soil CO2 efflux were similar to non-urban forests and grasslands in New Zealand, highlighting the importance of considering urban soils when assessing SOC stocks and soil CO2 efflux.

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.

The current changes in vehicle movement due to ‘lockdown’ conditions (imposed in cities worldwide in response to the COVID-19 epidemic) provide opportunities to quantify the local impact of ‘controlled interventions’ on air quality and establish baseline pollution concentrations in cities. Here, we present a case study from Auckland, New Zealand, an isolated Southern Hemisphere city, which is largely unaffected by long-range pollution transport or industrial sources of air pollution. In this city, traffic flows reduced by 60–80% as a result of a government-led initiative to contain the virus by limiting all transport to only essential services. In this paper, ambient pollutant concentrations of NO2, O3, BC, PM2.5, and PM10 are compared between the lockdown period and comparable periods in the historical air pollution record, while taking into account changes in the local meteorology. We show that this ‘natural experiment’ in source emission reductions had significant but non-linear impacts on air quality. While emission inventories and receptor modelling approaches confirm the dominance of traffic sources for NOx (86%), and BC (72%) across the city, observations suggest a consequent reduction in NO2 of only 34–57% and a reduction in BC of 55–75%. The observed reductions in PM2.5 (still likely to be dominated by traffic emissions), and PM10 (dominated by sea salt, traffic emissions to a lesser extent, and affected by seasonality) were found to be significantly less (8–17% for PM2.5 and 7–20% for PM10). The impact of this unplanned controlled intervention shows the importance of establishing accurate, local-scale emission inventories, and the potential of the local atmospheric chemistry and meteorology in limiting their accuracy., Graphical abstract Unlabelled Image, Highlights • The COVID-induced ‘natural experiment’ significantly impacted air quality. • The Isolated city of Auckland generates its own pollutants, with little to no long-range transport. • Significant reductions in NO2 & BC were observed while O3 concentrations increased. • Observed non-linear responses to changes in emissions.

We present a management and data correction framework for low-cost electrochemical sensors for nitrogen dioxide (NO2) deployed within a hierarchical network of low-cost and regulatory-grade instruments. The framework is founded on the idea that it is possible in a suitably configured network to identify a source of reliable ‘proxy’ data for each sensor site that has a similar probability distribution of measurement values over a suitable time period, and that sensor data can be checked and corrected by comparison of the sensor data distribution with that of the proxy. The framework is rule-based and easily modified. We use the reference network to choose proxies and check proxy reliability. We demonstrate the application of this methodology to low-cost instruments that use an electrochemical NO2 sensor together with a semiconducting oxide-based sensor for ozone (O3). The three NO2 sensor response parameters (offset, O3 response slope, and NO2 response slope) which are known to vary significantly as a consequence of ambient humidity and temperature variations, we show can be estimated by minimising statistical measures of divergence between sensor-estimated and proxy NO2 distributions over a 3-day window. We show how the parameter variations and statistical divergence measures with respect to the proxy can be used to indicate error conditions. The major error is due to a diurnally-varying, spatially-correlated offset term that is large for extremes of temperature, which we show can be estimated through its spatial correlation, using sensors co-located at reference sites. With these procedures, we demonstrate measurement at nine different locations across two regions of Southern California over seven months with average root mean square error ±7.2 ppb (range over locations 4–11 ppb) without calibration other than the remote proxy comparison. We apply the procedures to a network of 56 sensors distributed across the Inland Empire and Los Angeles County regions. The results show large variations in NO2 concentration taking place on short time- and distance scales across the region. These spatiotemporal NO2 variations were not captured by the more sparsely distributed regulatory network of air monitoring stations demonstrating the need for reliable data from dense networks of monitors to supplement the existing regulatory networks.

The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations where air quality differs from the expected land-use effect. The conditions and locations under which deviations were detected conform to expectations based on general experience., Comment: 26 pages, 11 figures

Background The Jaffe and enzymatic methods are the 2 most common methods for creatinine measurement. The Jaffe method is less expensive but subject to interferences. Some laboratory scientists have called for the Jaffe method to be retired. Objective To determine the most cost-effective and safe protocol for creatinine measurement. Method We performed a retrospective database review of all outpatient creatinine measurements for 1 year, testing the risk-based reflex testing protocol we had implemented for creatinine measurement. Samples were first measured using the Jaffe method and were reflexed to the enzymatic method if the estimated glomerular filtration rate (eGFR) was between 55 and 65 mL per min per 1.73 m2. Results There were 104,530 creatinine measurements, of which 11,420 (10.9%) were reflexed to the enzymatic method. The Jaffe method had a positive bias of 0.08 mg per dL (-6.14 mL/min/1.73 m2 eGFR). A total of 3.4% of the paired reflexed specimens were discordant. Also, 133 (1.2%) of the Jaffe results were classified as false negatives and 3411 (29.9%) were classified as false positives. None of the false-negative results and 5 of the false-positive results were considered clinically significant. Using the reflex protocol saved approximately $40,000 per year. Conclusions The reflex protocol for creatinine measurement can reduce costs with acceptable risk.

This paper explores the impact of choice of ventilation setting (“window open”, “new (external) air” and “recirculate”) on in-vehicle carbon monoxide exposures for commuters travelling by car at different times of the day (morning, midday, and evening) and different seasons (warm and cool) in Auckland, New Zealand. Three near-identical vehicles travelled in close proximity to each other on the same three “loops” out and into the city three times a day, each with a different ventilation setting. Concentrations of carbon monoxide were recorded using portable monitors placed inside each of the vehicles. The season was not found to be a significant factor. However, mean concentrations varied across ventilation settings by the time of day, typically peaking during the morning commute. The mean concentrations were significantly different between ventilation settings, with the recirculate setting found to result in a higher in-vehicle concentration than either new air or windows open but also heavily dependent on the initial in-vehicle concentration. However, this setting was the most effective at avoiding concentration spikes, especially when idling at intersections, an isolated peak event reaching 170 ppm was observed with the “new air” setting when following immediately behind an old, poorly-tuned, and visibly-emitting vehicle. This study suggests that having the windows open is the best setting for maintaining low in-cabin air pollution levels but that recirculate should be used in anticipation of congested conditions.

We provide a simple, remote, continuous calibration technique suitable for application in a hierarchical network featuring a few well-maintained, high-quality instruments ("proxies") and a larger number of low-cost devices. The ideas are grounded in a clear definition of the purpose of a low-cost network, defined here as providing reliable information on air quality at small spatiotemporal scales. The technique assumes linearity of the sensor signal. It derives running slope and offset estimates by matching mean and standard deviations of the sensor data to values derived from proxies over the same time. The idea is extremely simple: choose an appropriate proxy and an averaging-time that is sufficiently long to remove the influence of short-term fluctuations but sufficiently short that it preserves the regular diurnal variations. The use of running statistical measures rather than cross-correlation of sites means that the method is robust against periods of missing data. Ideas are first developed using simulated data and then demonstrated using field data, at hourly and 1 min time-scales, from a real network of low-cost semiconductor-based sensors. Despite the almost naïve simplicity of the method, it was robust for both drift detection and calibration correction applications. We discuss the use of generally available geographic and environmental data as well as microscale land-use regression as means to enhance the proxy estimates and to generalize the ideas to other pollutants with high spatial variability, such as nitrogen dioxide and particulates. These improvements can also be used to minimize the required number of proxy sites.

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.

Aiming at minimizing the costs, both of capital expenditure and maintenance, of an extensive air-quality measurement network, we present simple statistical methods that do not require extensive training data sets for automated real-time verification of the reliability of data delivered by a spatially dense hybrid network of both low-cost and reference ozone measurement instruments. Ozone is a pollutant that has a relatively smooth spatial spread over a large scale although there can be significant small-scale variations. We take advantage of these characteristics and demonstrate detection of instrument calibration drift within a few days using a rolling 72 h comparison of hourly averaged data from the test instrument with that from suitably defined proxies. We define the required characteristics of the proxy measurements by working from a definition of the network purpose and specification, in this case reliable determination of the proportion of hourly averaged ozone measurements that are above a threshold in any given day, and detection of calibration drift of greater than ±30% in slope or ±5 parts-per-billion in offset. By analyzing results of a study of an extensive deployment of low-cost instruments in the Lower Fraser Valley, we demonstrate that proxies can be established using land-use criteria and that simple statistical comparisons can identify low-cost instruments that are not stable and therefore need replacing. We propose that a minimal set of compliant reference instruments can be used to verify the reliability of data from a much more extensive network of low-cost devices.

Brown haze has been observed over a number of urban centres in different geographical locations around the world. It is a clear indication of degradation in air quality above a city; however, little is known about its underlying causes. This study examines the incidence of brown haze over the subtropical city of Auckland, New Zealand between 2001 and 2011. Using photographs to document the incidence and severity of the haze and a combination of near-surface meteorological observations, reanalysis data and three-dimensional modelling approaches, a detailed climatology of the incidence of haze is developed. The results show that haze is observed during weekday mornings in the cool months, most commonly associated with cold, calm conditions. These conditions are most commonly prevalent with the slow eastward movement of anticyclones (classified as Kidson weather types H and HSE). However, the conditional probability of haze events occurring within these classes is less than 25%, and there is little consistency in the thresholds of surface meteorological conditions between classes. Re-analysis of the mean sea-level pressure at regional to synoptic scales reveals that the strength and stability of the anticyclone is important in determining the occurrence and severity of brown haze events. Brown haze development is associated with a strong NE-SW temperature gradient immediately prior to the event, and anticyclonic development is enhanced by a well-developed wave train across the Southern Ocean. Back trajectory modelling shows these conditions are associated with slow moving air masses and suggest that the source of pollution is most likely to be local in origin. The results demonstrate the importance of a detailed understanding of synoptic and regional scale meteorology in predicting urban scale pollution events, even when the source of the pollution is local.

Globally, cities are growing at an unprecedented pace, putting pressure on space, existing infrastructure, and resources.[...]

The recent artworks Albedo of Clouds and Neighbourhood Air adopt weather as primary material for sensory experiences. The art installations included the contributions of scientists, programmers, instrument technicians, social online networks and the vagaries of the weather itself. The projects suggest that creative engagement with meteorological science can activate eco-political “networks” in Latour’s sense, productive of knowledge and potentially transformative. In such “meteorological art,” digital networks not only distribute facts about atmospheric data; they also generate affective forms. Multi-directional flows between weather instrumentation, digital data, media art and meteorological science are enacted in the pursuit of a creative outcome.

Portable low-cost instruments have been validated and used to measure ambient nitrogen dioxide (NO2) at multiple sites over a small urban area with 20min time resolution. We use these results combined with land use regression (LUR) and rank correlation methods to explore the effects of traffic, urban design features, and local meteorology and atmosphere chemistry on small-scale spatio-temporal variations. We measured NO2 at 45 sites around the downtown area of Vancouver, BC, in spring 2016, and constructed four different models: i) a model based on averaging concentrations observed at each site over the whole measurement period, and separate temporal models for ii) morning, iii) midday, and iv) afternoon. Redesign of the temporal models using the average model predictors as constants gave three 'hybrid' models that used both spatial and temporal variables. These accounted for approximately 50% of the total variation with mean absolute error±5ppb. Ranking sites by concentration and by change in concentration across the day showed a shift of high NO2 concentrations across the central city from morning to afternoon. Locations could be identified in which NO2 concentration was determined by the geography of the site, and others as ones in which the concentration changed markedly from morning to afternoon indicating the importance of temporal controls. Rank correlation results complemented LUR in identifying significant urban design variables that impacted NO2 concentration. High variability across a relatively small space was partially described by predictor variables related to traffic (bus stop density, speed limits, traffic counts, distance to traffic lights), atmospheric chemistry (ozone, dew point), and environment (land use, trees). A high-density network recording continuously would be needed fully to capture local variations.

Cycling and walking are being promoted in many urban areas as alternatives to motorised transport for health, environmental, and financial reasons. The reduced congestion and resulting decrease in the overall amount of pollution reduced can be expected to result in health benefits for the community. However, active commuters, due to their increased respiration rates and often increased travel times can expect to receive larger doses of air pollution compared with those using motorised forms of transport. However, given the large dropoff in concentrations away from a road, it can be expected that significant reductions can be achieved even with relatively small increases in separation between the path of cyclists/pedestrians and motor vehicles. This study presents a simple methodology for calculating the separation needed for cyclists and pedestrians to experience the same air pollution dose as car commuters. An example is given based on carbon monoxide (CO) data collected in a field campaign consisting of a car driver, a cyclist and a pedestrian travelling on a 2600 metre loop of road in Auckland. For this case study, the estimated distance from the centreline needed for cyclists and pedestrians to receive an equivalent dose of CO as motorists was found to range from 5.8 to 14.2 m depending on the commuting mode and the dispersion state of the atmosphere at the site. This was equal to a CO concentration reduction of 0.1–0.14 ppm per metre. Recommendations on facility modifications and route selections have been made to make active mode commuting safer.

This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration–wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p

Urban areas generally emit large amounts of anthropogenic carbon dioxide (CO2). However, studies that quantified the temporal and spatial variability of mean atmospheric CO2 concentrations and fluxes have suggested that dense vegetation may act as a local sink within cities. Consequently, urban greening programmes now form an important part of many urban climate change mitigation policies. Knowledge about the direct impact of urban vegetation and soils (collectively known as urban forests) on atmospheric CO2 concentrations is still limited and comprehensive measurement programmes are scarce. This article examines the methods currently used to quantify carbon (C) pools and CO2 fluxes of urban forests, and compiles currently available results. Whilst vegetation did not offset CO2 emissions on an annual basis in studies from mid-latitude cities, vegetative CO2 uptake contributed to the significantly lower atmospheric CO2 fluxes in summer. However, the currently available results are related to a large degree of uncertainty due to the limitations of the applied methods, the limited number of urban areas studied and the temporal/spatial resolution of the measurements. This article demonstrates that in order to effectively quantify CO2 fluxes from urban forests, future research needs to integrate data from a combination of methodologies collected at a range of scales.

Fundamental changes in the meaning and practice of environmental science are affecting – and are affected by – the theoretical, technological, pedagogical and institutional projects of physical geography. These changes have given rise to a range of ‘integrative’ (or integration-directed) disciplinary narratives which articulate a role for physical geographers within an engaged project of societal relevance and transformation. In this context, we welcome the rise of a notional ‘Critical Physical Geography’ and here we seek to expand the conversation to support thinking about what it might mean to be critical within physical geography. Moving beyond definitions of interdisciplinary collaboration, we propose that being critical from within physical geography begins with cultivating a critical disposition towards the situated partiality of our scientific practices. This prompts consideration of the ways in which our environmental objects could be assembled differently, reflecting different personal histories and values, and from different epistemic locations and management framings and through different investment narratives. A critical disposition prompts reflection upon the situated constraints and opportunities presented by our institutional locations. Recognition and articulation of critical perspectives may provoke endeavours to more consciously reassemble our scientific and institutional projects into more effective interventions to secure a more powerful and meaningful role for physical geographers across their diverse engagements.

Air pollution from the transport sector has a marked effect on human health, so isolating the pollutant contribution from a roadway is important in understanding its impact on the local neighbourhood. This paper proposes a novel technique based on a semi-empirical air pollution model to quantify the impact from a roadway on the air quality of a local neighbourhood using ambient records of a single air pollution monitor. We demonstrate the proposed technique using a case study, in which we quantify the contribution from a major highway with respect to the local background concentration in Auckland, New Zealand. Comparing the diurnal variation of the model-separated background contribution with real measurements from a site upwind of the highway shows that the model estimates are reliable. Amongst all of the pollutants considered, the best estimations of the background were achieved for nitrogen oxides. Although the multi-pronged approach worked well for predominantly vehicle-related pollutants, it could not be used effectively to isolate emissions of PM10 due to the complex and less predictable influence of natural sources (such as marine aerosols). The proposed approach is useful in situations where ambient records from an upwind background station are not available (as required by other techniques) and is potentially transferable to situations such as intersections and arterial roads. Applying this technique to longer time series could help to understand the changes in pollutant concentrations from the road and background sources for different emission scenarios, for different years or seasons. Modelling results also show the potential of such a hybrid semi-empirical models to contribute to our understanding of the physical parameters determining air quality and to validate emissions inventory data.