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The poor air quality in the London metropolis has sparked our interest in studying the time series dynamics of air pollutants in the city. The dataset consists of roadside and background air quality for seven standard pollutants: nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), ozone (O3), particulate matter (PM10 and PM2.5) and sulphur dioxide (SO2), using fractional integration to investigate issues such as persistence, seasonality and time trends in the data. Though we notice a large degree of heterogeneity across pollutants and a persistent behaviour based on a long memory pattern is observed practically in all cases. Seasonality and decreasing linear trends are also found in some cases. The findings in the paper may serve as a guide to air pollution management and European Union (EU) policymakers. [ABSTRACT FROM AUTHOR]

Abstract: Air pollution in urban areas has important implications for health and environmental management. Consequently, various methodologies have been developed for its assessment. Traditionally, simple approaches such as the box model or the Gaussian plume have been used to assess urban air quality. The use of Eulerian grid models, which provide a more realistic and comprehensive description of the urban atmosphere, has been increasing over the past few years. This paper reports on the use of the MM5–CMAQ modelling system to predict hourly ozone levels over London, UK. This work represents the first application of MM5–CMAQ in the UK for predicting air pollution levels in London. Domain resolutions of 9km, 3km and 1km (innermost) have been employed for this study. Summer periods in July and August 2002 have been simulated and the predicted results have been compared to several urban background stations across London. Input data for emissions have been derived from the UK National Emissions inventory and from the outputs of the EMIMO emission model. The CBM-IV chemical scheme has been used to simulate the atmospheric reactions for ozone. The model performance has been evaluated with measured data through a range of statistical measures. Although, the MM5–CMAQ model reproduces the ozone temporal trends it was not able to simulate the peak magnitudes consistently. Furthermore, for nighttime the model overestimated the ozone concentrations. The paper discusses the model performance and reasons for these discrepancies in terms of the various input data including emissions and meteorological parameters. [Copyright &y& Elsevier]

Abstract: In this paper we have coupled the CMAQ and ADMS air quality models to predict hourly concentrations of NO X , NO2 and O3 for London at a spatial scale of 20 m × 20 m. Model evaluation has demonstrated reasonable agreement with measurements from 80 monitoring sites in London. For NO2 the model evaluation statistics gave 73% of the hourly concentrations within a factor of two of observations, a mean bias of −4.7 ppb and normalised mean bias of −0.17, a RMSE value of 17.7 and an r value of 0.58. The equivalent results for O3 were 61% (FAC2), 2.8 ppb (MB), 0.15 (NMB), 12.1 (RMSE) and 0.64 (r). Analysis of the errors in the model predictions by hour of the week showed the need for improvements in predicting the magnitude of road transport related NO X emissions as well as the hourly emissions scaling in the model. These findings are consistent with recent evidence of UK road transport NO X emissions, reported elsewhere. The predictions of wind speed using the WRF model also influenced the model results and contributed to the daytime over prediction of NO X concentrations at the central London background site at Kensington and Chelsea. An investigation of the use of a simple NO–NO2–O3 chemistry scheme showed good performance close to road sources, and this is also consistent with previous studies. The coupling of the two models raises an issue of emissions double counting. Here, we have put forward a pragmatic solution to this problem with the result that a median double counting error of 0.42% exists across 39 roadside sites in London. Finally, whilst the model can be improved, the current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies. [Copyright &y& Elsevier]

Abstract: This paper addresses the research question “Are more stringent exhaust emissions standards, as applied to light vehicle type approval, resulting in reduced vehicle pollution in an urban area?” The exhaust emissions of a sample of over fifty thousand road vehicles operating in London were measured using roadside remote sensing absorption spectroscopy techniques (infrared and ultraviolet), combined with Automatic Number Plate Recognition for vehicle identification. Levels of carbon monoxide (CO), hydrocarbons (HC), nitric oxide (NO), and smoke (particulate) exhaust emissions are reported by vehicle class, fuel type, and Euro emissions standard. Emissions from petrol cars of each pollutant were all observed to display a statistically significant reduction with the introduction of each successive Euro emissions standard from Euro 1 onwards. However, Euro 2 diesel cars were observed to emit statistically higher rates of NO than either Euro 1 or Euro 3 standard diesel cars. The study also confirms the continuing ‘dieselisation’ of the UK passenger car fleet. Mean NO emissions from Euro 4 diesel cars were found to be 6 times higher than Euro 4 petrol cars, highlighting the need to develop a sound understanding of the current and future ‘in-use’ emissions characteristics of diesel vehicles, and their influence on local air quality. Smoke emissions from TXII London taxis (black cabs) were found to be statistically higher than either earlier TX1 or later TX4 model variants, with possible implications for local air quality policy interventions such as maximum age limits for taxis. [Copyright &y& Elsevier]

Abstract: Street-level mean flow and turbulence govern the dispersion of gases away from their sources in urban areas. A suitable reference measurement in the driving flow above the urban canopy is needed to both understand and model complex street-level flow for pollutant dispersion or emergency response purposes. In vegetation canopies, a reference at mean canopy height is often used, but it is unclear whether this is suitable for urban canopies. This paper presents an evaluation of the quality of reference measurements at both roof-top (height = H) and at height z = 9H = 190 m, and their ability to explain mean and turbulent variations of street-level flow. Fast response wind data were measured at street canyon and reference sites during the six-week long DAPPLE project field campaign in spring 2004, in central London, UK, and an averaging time of 10 min was used to distinguish recirculation-type mean flow patterns from turbulence. Flow distortion at each reference site was assessed by considering turbulence intensity and streamline deflection. Then each reference was used as the dependent variable in the model of which decomposes street-level flow into channelling and recirculating components. The high reference explained more of the variability of the mean flow. Coupling of turbulent kinetic energy was also stronger between street-level and the high reference flow rather than the roof-top. This coupling was weaker when overnight flow was stratified, and turbulence was suppressed at the high reference site. However, such events were rare (<1% of data) over the six-week long period. The potential usefulness of a centralised, high reference site in London was thus demonstrated with application to emergency response and air quality modelling. [Copyright &y& Elsevier]

Abstract: The paper describes a field study focused on the dispersion of a traffic-related pollutant within an area close to a busy intersection between two street canyons in Central London. Simultaneous measurements of airflow, traffic flow and carbon monoxide concentrations ([CO]) are used to explore the causes of spatial variability in [CO] over a full range of background wind directions. Depending on the roof-top wind direction, evidence of both flow channelling and recirculation regimes were identified from data collected within the main canyon and the intersection. However, at the intersection, the merging of channelled flows from the canyons increased the flow complexity and turbulence intensity. These features, coupled with the close proximity of nearby queuing traffic in several directions, led to the highest overall time-average measured [CO] occurring at the intersection. Within the main street canyon, the data supported the presence of a helical flow regime for oblique roof-top flows, leading to increased [CO] on the canyon leeward side. Predominant wind directions led to some locations having significantly higher diurnal average [CO] due to being mostly on the canyon leeward side during the study period. For all locations, small changes in the background wind direction could cause large changes in the in-street mean wind angle and local turbulence intensity, implying that dispersion mechanisms would be highly sensitive to small changes in above roof flows. During peak traffic flow periods, concentrations within parallel side streets were approximately four times lower than within the main canyon and intersection which has implications for controlling personal exposure. Overall, the results illustrate that pollutant concentrations can be highly spatially variable over even short distances within complex urban geometries, and that synoptic wind patterns, traffic queue location and building topologies all play a role in determining where pollutant hot spots occur. [Copyright &y& Elsevier]

Public transport is commonly associated with benefits such as reducing road traffic congestion and improving air quality. This paper focuses on evaluating the causal impact of a new public transport provision in London, the Jubilee Line Extension (JLE) in 1999, on air quality. Using meteorological normalisation and a regression discontinuity design with time as the forcing variable, we show that the JLE led to only small changes in air pollution at some specific locations; detectable changes in NO x , NO 2 , and O 3 concentrations were found at 63%, 43% and 29% of air pollution monitoring sites, respectively. For those sites where a change in pollution was detected, the responses ranged from −2% to +1% for NO 2 and -1% to 0% for O 3. We calculate that the long-run effects are greater, ranging from −11% to +3% for NO 2 and from −2% to +2% for O 3 at sites that showed a response to the JLE. Aggregating across all sites in London for a city-wide effect, both short and long-run effects were less than 1% or insignificant. We find statistically significant increases in NO 2 and O 3 concentrations at some background sites, but the magnitude of effect is within +1% in the short-run and +3% in the long-run. Our analysis shows that the effect of the JLE on air pollution in some areas was greater than others, however across London the effect was small and this indicates that public transport provision on its own is not an effective strategy to improve air quality. • An increase in public transport supply did not improve air quality at all sites. • Public transport provision on its own is not effective to improve air quality. • Short-run changes in pollution concentrations ranged from −2% to +1%. • Long-run changes in pollution concentrations may range from −11% to +3%. [ABSTRACT FROM AUTHOR]

Outdoor air pollution remains a major environmental threat to the public, especially those who reside in highly urbanised areas. Recent studies have revealed the effectiveness of early-warning mechanisms that enable the public reduce their exposure to air pollutants. This highlights the need for accurate air quality forecasts. However, air quality in many developing and highly urbanised countries remains unmonitored. Hence, a novel spatiotemporal interpolation modelling approach based on a deep learning and wavelet pre-processing technique was proposed in this paper. In more detail, Long Short-term Memory (LSTM) neural networks and Discrete Wavelet Transformation (DWT) were utilised to model the spatial variability of hourly NO 2 levels at six urban sites in Central London, the United Kingdom. The models were trained using only the NO 2 concentration data from the neighbouring sites. Benchmark models such as plain LSTM and Multilayer Perceptron (MLP) models were also developed to validate the effectiveness of the proposed models. The proposed wavelet-based spatiotemporal models were found to provide superior forecasting results, explaining 77% to 93% of the variability of the actual NO 2 concentration levels at most sites. The overall results reveal the very promising potential of the proposed models for the spatiotemporal characterisation of outdoor air pollution. [ABSTRACT FROM AUTHOR]

Air pollution contributes to 9400 deaths annually in London and diesel vehicles are considered a major source of lethal air pollutants. Consequently, the UK government announced its intention to ban diesel vehicles by 2040 to achieve a sustainable zero-carbon road transport system. Since no empirical studies have used a bottom-up approach to seek Londoners' views, it is therefore worth investigating the public opinion regarding this forthcoming ban. This paper aims to fill this research gap by taking London as a case study. A survey was designed, and fieldwork was conducted to distribute questionnaires to Londoners. Completed questionnaires were analysed using both quantitative and qualitative methods. The findings revealed that the majority of Londoners would be in favour of the ban if they were sufficiently exposed to the appropriate sources of information and were favourably disposed towards environmental protection measures. The results also showed that Londoners were more likely to switch to electric vehicles (EVs) if they were offered generous incentives and encouraged to use scrappage schemes. The present study makes a strong case for enforcing the ban well before 2040. The significance of this research is to provide clearer signals regarding the future of diesel vehicles, which in turn will strengthen the EV policy and uptake. [ABSTRACT FROM AUTHOR]