Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area,Australia

Land use regression models are an established method for estimating spatial variability in gaseous pollutant levels across urban areas. Existing LUR models have been developed to predict annual average concentrations of airborne pollutants. None of those models have been developed to predict daily average concentrations, which are useful in health studies focused on the acute impacts of air pollution. In this study, we developed LUR models to predict daily NO₂ and NO_x concentrations during 2009–2012 in the Brisbane Metropolitan Area (BMA), Australia's third-largest city. The final models explained 64% and 70% of spatial variability in NO₂ and NO_x, respectively, with leave-one-out-cross-validation R² of 3–49% and 2–51%. Distance to major road and industrial area were the common predictor variables for both NO₂ and NO_x, suggesting an important role for road traffic and industrial emissions. The novel modeling approach adopted here can be applied in other urban locations in epidemiological studies.     

Modelling the Spatio-Temporal Distribution of Ambient Nitrogen Dioxide and Investigating the Effects of Public Transit Policies on Population Exposure

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NO_x) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO₂) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO₂ concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO₂ hot spots. This is associated with a reduction in total NO_x emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO₂ concentration across the study domain and the mean NO₂ exposure across the sample population results from the fact that NO₂ concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains.     

基于GIS的西安市城镇建设用地扩展研究

基于1988年TM影像、2002年ETM影像和2007年TM影像提取了西安市主城区和远郊区城镇建设用地信息,利用GIS技术对三期城镇建设用地进行了叠加得到西安市城镇扩展数据。利用主要道路交通图和行政边界图对扩展数据进行裁切,得到了西安市绕城高速内和远郊区的城镇扩展信息。运用扩展强度指数、城镇建设用地相对变化率和分形维数等模型对西安市的城镇扩展进行了分析研究。研究结果表明,研究区内的城镇建设用地面积从1988年的151 796 493.8 m2增加到2007年的365 180 608.0 m2,总体扩展了2.4倍。其中主城区的扩展主要集中在二环与绕城高速之间,二环内的城镇建设用地面积扩展速度相对较慢;西安市的3个远郊区中长安区扩展倍数最大,在1988~2007年扩展了7.39倍,其次为临潼区和阎良区;1988~2002年西安市的主城区和远郊区的分形维数都呈增加的趋势,城市边缘形态趋于复杂,而2002~2007年分形维数呈减少的趋势。西安市远郊区的城镇建设用地相对变化率高于主城区,未来城市发展的格局逐渐呈现为主城区城镇扩展速度的相对放缓和远郊区的规模不断增大的趋势。     

Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO_x) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO_x from passenger cars was 10.2 μg/m³; individual exposure concentrations ranged from 0.2 to 145 μg/m³. Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.     

A pixel‐based method to estimate urban compactness and its preliminary application

A pixel‐based method was designed to estimate urban compactness. Remote sensing technology was used to extract urban land use, while urban pixels in the classification image were directly used to estimate urban compactness. The method was based on the assumption that an urban area in circular form was the most compact. Forty virtual circular cities with different radii were fabricated in order to derive a formula to quantify urban compactness and a curve function in the form of y = 6.9351x ^2.0039 can thus be derived by using two data: average radius and area. Obviously, this curve can be applied to countless virtual cities in circular form of the same compactness but with different radii. Actually, for each real city in the classification image, specific urban area and average distance can be calculated directly. For every specific data pairs being thus derived, an exclusive curve function in the general form of y = 6.9351x^D can be found. Our studies show that the value of D in the curve function could be used to quantify urban compactness.     

Low and high NOx chemical regimes in an urban environment

In this work, a chemistry-transport box model, derived from the CHIMERE model, is used in order to examine the existence of different chemical regimes, known as the low NO_x and high NO_x regimes, and the existence of multiple equilibria in an urban environment. From an operationnal point of view, the existence of chemical regimes of low NO_x and high NO_x type and of multiple equilibria is of paramount importance for the effectiveness of the air quality policies. For the urban photochemical system under consideration, we determine the equilibria associated with primary pollutant emissions varying in a range of values typical of the parisian urban area. The influence of several variables on these regimes is also studied, through the following parameters: wind speed, upstream ozone concentration, cloudiness.     

Dynamic parameter estimation for a street canyon air quality model

The Operational Street Pollution Model (OSPM^®) is a widely used air quality model for urban street canyons. It is a parametric model, simulating the contribution from traffic emissions on a single street at receptor points at the buildings' facades. The OSPM contains a number of empirical parameters, accounting for processes such as emission factors or dispersion of pollutants. The values of these parameters are based on empirical assumptions, and might not be optimal for a specific street. In this work, we allow these parameters to vary within a certain meaningful range.We implemented two different parameter estimation schemes: a dynamic estimation procedure (using an ensemble Kalman filter) that allowed parameter values to vary, and a static estimation procedure scheme (using a least-squares algorithm) that kept parameter values fixed during the course of the simulation. We ran year-long simulations for five different streets in Danish cities, and evaluated performance by comparing forecast concentrations of NO_x, NO₂, O₃ and CO with observations.Overall, the parameter estimation substantially improved the performance of the model in forecasting, especially for NO₂ and CO. However it led to slightly more bias in the modelled daily maximum concentrations, suggesting that the parameter estimation fits to the bulk of the data rather than the extremes. Estimated parameter values varied substantially in time and between sites, making it difficult to generalise parameter estimates to other locations. Modelled concentrations from the OSPM were, on average, notably more accurate in simulations using measured urban background concentrations and meteorological parameters compared to using modelled data for these inputs. However this is only applicable when observations from nearby meteorological and urban background monitoring sites are available.We conclude that although dynamic parameter estimation has limited applicability to real-time air quality forecasting, it can potentially give useful feedback about the quality of model parameterisations or model inputs. Static parameter estimation is a simpler method, which is often as effective as dynamic parameter estimation.     

Impact of Traffic Management on Black Carbon Emissions: a Microsimulation Study

This paper investigates the effectiveness of traffic management tools, including traffic signal control and en-route navigation provided by variable message signs (VMS), in reducing traffic congestion and associated emissions of CO₂, NO_x, and black carbon. The latter is among the most significant contributors of climate change, and is associated with many serious health problems. This study combines traffic microsimulation (S-Paramics) with emission modeling (AIRE) to simulate and predict the impacts of different traffic management measures on a number traffic and environmental Key Performance Indicators (KPIs) assessed at different spatial levels. Simulation results for a real road network located in West Glasgow suggest that these traffic management tools can bring a reduction in travel delay and BC emission respectively by up to 6 % and 3 % network wide. The improvement at local levels such as junctions or corridors can be more significant. However, our results also show that the potential benefits of such interventions are strongly dependent on a number of factors, including dynamic demand profile, VMS compliance rate, and fleet composition. Extensive discussion based on the simulation results as well as managerial insights are provided to support traffic network operation and control with environmental goals. The study described by this paper was conducted under the support of the FP7-funded CARBOTRAF project.     

Model study of tropospheric composition response to NOx and CO pollution

The effect of NO_x and CO pollution on other important atmospheric gases from 0–16 km in the northern temperate zonal belt is calculated using a 2-D (altitude–longitude) channel photochemical model with climatic zonal and vertical fixed transport. The geographical inhomogeneities of the NO_x and CO large-scale surface releases are modeled. The distinction between NO_x and CO fluxes from the oceanic and land surfaces and those from areas with various pollution source intensities is considered. NO_x and CO emissions from world transport aviation engine exhausts and the NO source from lightning discharges are also included. Model results are analyzed and compared with observational data for nitrogen compounds, carbon monoxide, and ozone. The dependence of the spatial and temporal hydroxyl distribution on the carbon monoxide (as the main destroyer of OH) concentration field is analyzed and discussed.     

Assessment of NO2 satellite observations for en-route aircraft emissions detection

In this work we examine the possibility of using satellite remote sensors for the detection of air traffic emissions produced during the en-route segment of flight in the Upper Troposphere/Lower Stratosphere region (8000-12,000 m). NO₂ has been considered as the tracer of aircraft plumes with highest possibility of being successfully detected from space. An analysis of the technical potential of the current orbital sensors capable of measuring NO₂ in the proximity of the tropopause has been conducted. In order to estimate an upper bound for the NO₂ column related to aircraft emissions, the Canary Islands Corridor has been selected for conducting a simple emission calculation exercise based on real air traffic and operational data, assuming an ideal atmospheric scenario. The results obtained in this approximation have been compared to the actual information retrieved from space sensors. An in-depth inspection of the NO₂ column data for two particular areas (Canary Islands Corridor and North Atlantic Flight Corridor) obtained in recent years by SCIAMACHY and OMI has also been carried out.The general conclusions of this viability study are not optimistic. The estimated maximum NO₂ column value attributable to aircraft emissions at cruise altitudes were lower than the detection limits associated with SCIAMACHY and OMI for NO₂ column measurements. As a result, detecting and quantifying the actual NO₂ levels in aircraft corridors by space remote sensing is a very challenging task.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

Semi-physical mean-value NOx model for diesel engine control

A semi-physical model has been developed to predict nitrogen oxide (NO_x) emissions produced by diesel engines. This model is suitable for online NO_x estimation and for model-based engine control. It is derived from a zero-dimensional thermodynamic model which was simplified by only retaining main phenomena contributing to NO_x formation. The crank angle evolution of the burned gas temperature, which has a strong impact on NO_x formation rate, is described by a semi-empirical model whose key variable is the maximum burned gas temperature. This variable presents a good correlation with the molar fraction of NO_x at the end of combustion and can be expressed as a function of the intake burned gas ratio and the start of combustion. The maximum burned gas temperature sub-model is then coupled to an averaged NO_x formation kinetic model (based on the Zeldovich mechanism) to form a mean-value model for NO_x computation. This latter model was validated using data sets recorded in two diesel engines for steady-state operating conditions as well as for several driving cycles including parametric variations of the engine calibration.     

Prediction and reduction of diesel engine emissions using a combined ANN–ACO method

In this study, the combination of artificial neural network (ANN) and ant colony optimization (ACO) algorithm has been utilized for modeling and reducing NO_x and soot emissions from a direct injection diesel engine. A feed-forward multi-layer perceptron (MLP) network is used to represent the relationship between the input parameters (i.e., engine speed, intake air temperature, rate of fuel mass injected, and power) on the one hand and the output parameters (i.e., NO_x and soot emissions) on the other hand. The ACO algorithm is employed to find the optimum air intake temperatures and the rates of fuel mass injected for different engine speeds and powers with the purpose of simultaneous reduction of NO_x and soot. The obtained results reveal that the ANN can appropriately model the exhaust NO_x and soot emissions with the correlation factors of 0.98, 0.96, respectively. Further, the employed ACO algorithm gives rise to 32% and 7% reduction in the NO_x and soot, respectively. The response time of the optimization process was obtained to be less than 4 min for the particular PC system used in the present work. The high accuracy and speed of the model show its potential for application in intelligent controlling systems of the diesel engines.     

Detection and emission estimates of NOx sources over China North Plain using OMI observations

We designed a fast procedure to detect the nitrogen oxides (NO_x) sources in the China North Plain and to estimate their NO_x emissions through a two-dimensional Gaussian fitting method applied to averaged Ozone Monitoring Instrument (OMI) observations of nitrogen dioxide (NO₂) column concentration. The Northern China Plain is a region that has one of the highest densities of anthropogenic NO_x sources in the world and therefore the sources are difficult to distinguish. With our procedure we still found 94 individual NO_x emission sources. Of these sources Tangshan city has the strongest NO_x emission rate (92 Gg N year^–1), while the weakest that we are still able to detect is Zhangjiakou city, with a NO_x emission rate of 0.4 Gg N year^–1. Using the fitting results, we reconstruct the NO₂ column concentration distribution map, which matches the OMI observations with an R² = 0.85 and a slope of 0.78. The derived NO_x emission rates for cities and provinces level show good agreement with former studies.     

Investigating the sensitivity of health benefits to focussed PM2.5 emission abatement strategies

Modelling, pollution monitoring and epidemiological studies all have a role to play in developing effective policies to improve air quality and human health. Epidemiological studies have shown that of particular importance are the effects of fine particulate matter, PM₁₀ and PM_2.5 which can penetrate into human lungs. At present it is not clear which components of PM are responsible for health effects although toxicological studies have identified several potential factors. Hence, based on WHO guidance, current legislation has focused on the total mass, with the EC setting limit values on total PM₁₀, followed by target reductions for population exposure to PM_2.5 in urban agglomerations. Trends in measured concentrations at selected urban monitoring stations are required as evidence for achievement of these reductions. This paper addresses these issues at the borough level in London using the integrated assessment model UKIAM, developed originally for application at the national scale, with illustrations comparing abatement of two contrasting sources – domestic combustion and road transport. The former, dominated by natural gas generating NO_X emissions, contributes to longer range secondary PM formation extending beyond the city. The latter is an important source of black carbon as a primary pollutant causing local exposure, as well as NO_X. WHO data is used in relation to impacts of particle concentrations by mass, and response functions for black carbon are taken from the literature. The results show that from a city perspective there are enhanced benefits from reducing the road transport emissions, especially with regard to potential toxicity of black carbon. The scenarios modelled also highlight the spatial variations of benefits across London, and illustrate deviations from trends as represented by limited monitoring data from the different boroughs, together with the influence upon exposure of mobile population within the city.     

Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics

Due to the worldwide trend of industrialization and urbanization, air pollutants were emitted heavily on a global scale particularly in developing countries, which produces adverse effects on human health by causing health problems such as respiratory and lung diseases. Many regression models based on land use types and urban fabrics have been built to analyze the spatiotemporal distribution of air pollutants, however, few of them examined the relationship between urban morphological characteristics and the distribution of air pollutants in a megacity. This study investigates such relationships for six types of air pollutants (PM2.5, PM10, SO₂, NO₂, O₃, and CO) and a composite AQI (Air Quality Index) based on hourly data at 35 monitoring stations in Beijing in 2016, with morphological characteristics (Morphological building index), meteorological factors (Land Surface Temperature, LST), land use (vegetation, road length, gas station and industry point data), and population distribution data. We also analyzed the results with spatiotemporal regression and SSH (Spatial Stratified Heterogeneity) models respectively. According to the spatiotemporal regression model, the morphological building index (MBI) shows a strong correlation with the dispersion of PM2.5 (R² = 0.81) and AQI (R² = 0.80) in the warm season and this finding was reinforced through the Leave-one-out-cross-validation (LOOCV) analysis. From the SSH analysis, the road length in a large proximal region impacts air pollutants the most, especially for O₃; and population density significantly affects PM 2.5, AQI, SO₂, and NO₂ in the cold season. From an integrated interpretation, distance to nearest industry impacts the spatial distribution of NO₂ in cold season, while it impacts that of PM2.5 and AQI in both warm and cold seasons. The research finds that these two models supplement each other well and together help to give us a better understanding of how air quality is affected in the urban landscape.     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Observations of urban NOx plume dispersion using mobile and satellite DOAS measurements around the megacity of St.Petersburg (Russia)

Mobile DOAS (Differential Optical Absorption Spectroscopy) circular measurements of tropospheric nitrogen dioxide (NO₂) were performed on a number of days in 2012 and 2014–2016 around St.Petersburg. These observations figured out an evolution of urban pollution plume, released from the megacity. The HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectories) model was configured to simulate the observed NO₂ dispersion, taking into account the municipal inventory database of urban nitrogen oxides (NO_x = NO+ NO₂) emission. The simulation results were found consistent with the data of mobile measurements, allowing to fit an estimate of integral NO_x emission rate from St.Petersburg (about 60 kt year^?1 on the average). Coincident satellite measurements the Aura Ozone Monitoring Instrument (OMI) of tropospheric NO₂ mostly agree with the data of simulation on the days of mobile observations. In general, presented results demonstrate the capabilities of joint interpretation of disparate tropospheric NO₂ data in the vicinity of a megacity.     

Aviation emission inventory development and analysis

An up to date and accurate aviation emission inventory is a prerequisite for any detailed analysis of aviation emission impact on greenhouse gases and local air quality around airports. In this paper we present an aviation emission inventory using real time air traffic trajectory data. The reported inventory is in the form of a 4D database which provides resolution of 1° × 1° × 1000 ft for temporal and spatial emission analysis. The inventory is for an ongoing period of six months starting from October 2008 for Australian Airspace.In this study we show 6 months of data, with 492,936 flights (inbound, outbound and over flying). These flights used about 2515.83 kt of fuel and emitted 114.59 kt of HC, 200.95 kt of CO, 45.92 kt of NO_x, 7929.89 kt of CO₂, and 2.11 kt of SO_x. From the spatial analysis of emissions data, we found that the CO₂ concentration in some parts of Australia is much higher than other parts, especially in some major cities. The emission results also show that NO_x emission of aviation may have a significant impact on the ozone layer in the upper troposphere, but not in the stratosphere.It is expected that with the availability of this real time aviation emission database, environmental analysts and aviation experts will have an indispensable source of information for making timely decisions regarding expansion of runways, building new airports, applying route charges based on environmentally congested airways, and restructuring air traffic flow to achieve sustainable air traffic growth.     

Vanadia doped tungsten-titania SCR catalysts as functional materials for exhaust gas sensor applications

Urea-SCR systems (selective catalytic reduction) are required to meet future NO_x emission standards of heavy-duty and light-duty vehicles. It is a key factor to control the SCR systems and to monitor the catalysts’ functionalities to achieve low emissions. The novel idea of this study is to apply commercially available SCR catalyst materials based on vanadia-doped tungsten-titania as gas sensing films for impedimetric thick-film exhaust gas sensor devices. The dependence of the impedance on the surrounding gas atmosphere, especially on the concentrations of NH₃ and NO₂, is investigated, as well as cross interferences from other components of the exhaust. The sensors provide a good NH₃ sensitivity at 500 °C. The sensor behavior is explained in light of the literature combining the fields of catalysts and semiconducting gas sensors.