A large number of policy initiatives are being taken at the European level, across the United Kingdom and in London to improve air quality and reduce population exposure to harmful pollutants from ...traffic emissions. Trends in roadside increments of nitrogen oxides (NOX), nitrogen dioxide (NO2), particulate matter (PM), black carbon (CBLK) and carbon dioxide (CO2) were examined at 65 London monitoring sites for two periods of time: 2005–2009 and 2010–2014. Between 2005 and 2009 there was an overall increase in NO2 reflecting the growing evidence of real world emissions from diesel vehicles. Conversely, NO2 decreased by 10%·year−1 from 2010 onwards along with PM2.5 (−28%·year−1) and black carbon (−11%·year−1). Downwards trends in air pollutants were not fully explained by changes in traffic counts therefore traffic exhaust emission abatement policies were proved to be successful in some locations. PM10 concentrations showed no significant overall change suggesting an increase in coarse particles which offset the decrease in tailpipe emissions; this was especially the case on roads in outer London where an increase in the number of Heavy Good Vehicles (HGVs) was seen. The majority of roads with increasing NOX experienced an increase in buses and coaches. Changes in CO2 from 2010 onwards did not match the downward predictions from reduced traffic flows and improved fleet efficiency. CO2 increased along with increasing HGVs and buses. Polices to manage air pollution provided differential benefits across London's road network. To investigate this, k-means clustering technique was applied to group roads which behaved similarly in terms of trends to evaluate the effectiveness of policies to mitigate traffic emissions. This is the first time that London's roadside monitoring sites have been considered as a population rather than summarized as a mean behaviour only, allowing greater insight into the differential changes in air pollution abatement policies.
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•Short-term trends in roadside increments of air pollutants along 65 roads in London.•Great variability of response to abatement policies across London's roads.•General decrease of NOX and NO2 concentrations in 2010–2014.•Roads with increased NOX observed more buses and heavy good vehicles.•PM2.5 concentrations decreased along with black carbon; PM10 coarse increased.
Trends in roadside air pollutants in London: results from a large population of monitoring sites.
Keeping air pollution policies on track Fuller, Gary W; Font, Anna
Science (American Association for the Advancement of Science),
2019-Jul-26, 2019-07-26, 20190726, Letnik:
365, Številka:
6451
Journal Article
Recenzirano
Focusing on trends rather than compliance can lead to more effective policies
Around the world, thousands of instruments are measuring air pollution. The World Air Quality website (
1
) provides ...near-real time air pollution data from stations across North America, Europe, and through the Middle East to Southeast Asia and across Australia and New Zealand. Occasionally, one of these data points leaps into the news. Beijing frequently made the headlines in the early part of this decade but, for the past 2 years, Delhi has started to dominate newspaper reports as crop burning in the surrounding area adds to the city's considerable air pollution woes. Like many cities, Delhi now faces huge challenges to reverse its deteriorating air pollution. Most air pollution policies focus on compliance with specific pollution limits, but progress has often been slow. More effective progress may be achieved by focusing on the rate of change.
Paris and London are Europe's two megacities and both experience poor air quality with systemic breaches of the NO2 limit value. Policy initiatives have been taken to address this: some European-wide ...(e.g. Euro emission standards); others local (e.g. Low Emission Zone, LEZ). Trends in NOX, NO2 and particulate matter (PM10, PM2.5) for 2005–2016 in background and roadside locations; and trends in traffic increments were calculated in both cities to address their impact. Trends in traffic counts and the distribution in Euro standards for diesel vehicles were also evaluated. Linear-mixed effect models were built to determine the main determinants of traffic concentrations. There was an overall increase in roadside NO2 in 2005–2009 in both cities followed by a decrease of ∼5% year−1 from 2010. Downward trends were associated with the introduction of Euro V heavy vehicles. Despite NO2 decreasing, at current rates, roads will need 20 (Paris) and 193 years (London) to achieve the European Limit Value (40 μg m−3 annual mean). Euro 5 light diesel vehicles were associated with the decrease in roadside PM10. An increase in motorcycles in London since 2010 contributed to the lack of significant trend in PM2.5 roadside increment in 2010–16.
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•NO2 and PM concentrations from traffic decreased from 2010–16 in Paris and London.•Euro V diesel heavy vehicles induced downward trends in roadside NO2.•Effective reduction in traffic PM10 concentrations with of Euro 5 diesel lights.•London observed faster decrease on PM10 concentrations from traffic than Paris.•An increase in motorcycles in London caused higher roadside PM2.5 concentrations.
Trends in air pollutants in Europe's megacities.
Vehicle-related particulate matter (PM) emissions may arise from both exhaust and non-exhaust mechanisms, such as brake wear, tire wear, and road pavement abrasion, each of which may be emitted ...directly and indirectly through resuspension of settled road dust. Several researchers have indicated that the proportion of PM2.5 attributable to vehicle traffic will increasingly come from non-exhaust sources. Currently, very little empirical data is available to characterize tire and road wear particles (TRWP) in the PM2.5 fraction. As such, this study was undertaken to quantify TRWP in PM2.5 at roadside locations in urban centers including London, Tokyo and Los Angeles, where vehicle traffic is an important contributor to ambient air PM. The samples were analyzed using validated chemical markers for tire tread polymer based on a pyrolysis technique. Results indicated that TRWP concentrations in the PM2.5 fraction were low, with averages ranging from < 0.004 to 0.10 µg/m3, representing an average contribution to total PM2.5 of 0.27%. The TRWP levels in PM2.5 were significantly different between the three cities, with significant differences between London and Los Angeles and Tokyo and Los Angeles. There was no significant correlation between TRWP in PM2.5 and traffic count. This study provides an initial dataset to understand potential human exposure to airborne TRWP and the potential contribution of this non-exhaust emission source to total PM2.5.
•Ultrafine particle sources were identified and quantified in four European cities.•Common sources were Photonucleation, different Traffic sources, and Secondary.•Maximum contribution of traffic ...sources ranged 71–94% of total particle number.•Airport emissions contributed to nucleation particles in urban background areas.
Ultrafine particles (UFP) are suspected of having significant impacts on health. However, there have only been a limited number of studies on sources of UFP compared to larger particles. In this work, we identified and quantified the sources and processes contributing to particle number size distributions (PNSD) using Positive Matrix Factorization (PMF) at six monitoring stations (four urban background and two street canyon) from four European cities: Barcelona, Helsinki, London, and Zurich. These cities are characterised by different meteorological conditions and emissions. The common sources across all stations were Photonucleation, traffic emissions (3 sources, from fresh to aged emissions: Traffic nucleation, Fresh traffic – mode diameter between 13 and 37 nm, and Urban – mode diameter between 44 and 81 nm, mainly traffic but influenced by other sources in some cities), and Secondary particles. The Photonucleation factor was only directly identified by PMF for Barcelona, while an additional split of the Nucleation factor (into Photonucleation and Traffic nucleation) by using NOx concentrations as a proxy for traffic emissions was performed for all other stations. The sum of all traffic sources resulted in a maximum relative contributions ranging from 71 to 94% (annual average) thereby being the main contributor at all stations. In London and Zurich, the relative contribution of the sources did not vary significantly between seasons. In contrast, the high levels of solar radiation in Barcelona led to an important contribution of Photonucleation particles (ranging from 14% during the winter period to 35% during summer). Biogenic emissions were a source identified only in Helsinki (both in the urban background and street canyon stations), that contributed importantly during summer (23% in urban background). Airport emissions contributed to Nucleation particles at urban background sites, as the highest concentrations of this source took place when the wind was blowing from the airport direction in all cities.
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•Particle size distributions and noise levels were measured at two locations near Gatwick airport in 2018–19.•Peak particle number concentrations (PNC) were highest at the site closer ...to the runway.•Source apportionment identified six factors at each site with the airport source factor contributing 17%.•The largest source of noise above background was associated with sources of fresh traffic and urban particles depending on the site.•PNC is unlikely to be an important confounder in epidemiological studies of aircraft noise and health near airports.
There is increasing evidence of potential health impacts from both aircraft noise and aircraft-associated ultrafine particles (UFP). Measurements of noise and UFP are however scarce near airports and so their variability and relationship are not well understood. Particle number size distributions and noise levels were measured at two locations near Gatwick airport (UK) in 2018–19 with the aim to characterize particle number concentrations (PNC) and link PNC sources, especially UFP, with noise. Positive Matrix Factorization was used on particle number size distribution to identify these sources. Mean PNC (7500–12,000 p cm−3) were similar to those measured close to a highly trafficked road in central London. Peak PNC (94,000 p cm−3) were highest at the site closer to the runway. The airport source factor contributed 17% to the PNC at both sites and the concentrations were greatest when the respective sites were downwind of the runway. However, the main source of PNC was associated with traffic emissions. At both sites noise levels were above the recommendations by the WHO (World Health Organisation). Regression models of identified UFP sources and noise suggested that the largest source of noise (LAeq-1hr) above background was associated with sources of fresh traffic and urban UFP depending on the site. Noise and UFP correlations were moderate to low suggesting that UFP are unlikely to be an important confounder in epidemiological studies of aircraft noise and health. Correlations between UFP and noise were affected by meteorological factors, which need to be considered in studies of short-term associations between aircraft noise and health.
Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their ...large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm−3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016–2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from −18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.
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•The first time that transferability of LUR models for UFP within a megacity has been evaluated•Repeated 30-min monitoring of UFP particle number at 40 sites in three residential areas in London•Single- and two-area models were transferred between residential areas in London.•Performance of models within areas for which they were developed was moderate to good•Models without calibration performed poorly when transferred to other areas due to increases in bias.
Ahead of measures to incentivise wood heating, the current level of wood burning in London was assessed by two tracer methods; i) a six week campaign of daily measurements of levoglucosan along a ...38 km transect across the city during winter 2010, ii) a three year (2009–2011) measurement programme of black carbon and particulate matter from wood burning using differential IR and UV absorption by Aethalometer. Mean winter levoglucosan concentrations were 160 ± 17 ng m−3 in central London and 30 ± 26 ng m−3 greater in the suburbs, with good temporal correlation (r2 = 0.68–0.98) between sampling sites. Sensitivity testing found that the aethalometer wood burning tracer method was more sensitive to the assumed value of the Ångström coefficient for fossil fuel black carbon than it was to the Ångström coefficient for wood burning PM, and that the model was optimised with Ångström coefficient for fossil fuel black carbon of 0.96. The aethalometer and levoglucosan estimates of mean PM from wood burning were in good agreement during the winter campaign; 1.8 μg m−3 (levoglucosan) and 2.0 μg m−3 (aethalometer); i.e. between 7% and 9% of mean PM10 across the London transect. Analysis of wood burning tracers with respect to wind speed suggested that wood burning PM was dominated by sources within the city. Concentrations of aethalometer and levoglucosan wood burning tracers were a greatest at weekends suggesting discretionary or secondary domestic wood burning rather than wood being used as a main heating source. Aethalometer wood burning tracers suggests that the annual mean concentration of PM10 from wood burning was 1.1 μg m−3. To put this in a policy context, this PM10 from wood burning is considerably greater than the city-wide mean PM10 reduction of 0.17 μg m−3 predicted from the first two phases of the London Low Emission Zone which was introduced to reduce PM from traffic sources.
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•Aethalometer and levoglucosan methods used to estimate the contribution of wood smoke to PM10 in London.•Annual mean PM10 from wood burning in London was 1.1 μg m−3.•PM was most likely from a mixture of wood types burnt as decorative or secondary heating.
Background: Epidemiologic evidence suggests that exposure to ambient particulate matter is associated with adverse health effects. Little is known, however, about which components of the particulate ...mixture (size, number, source, toxicity) are most relevant to health. We investigated associations of a range of particle metrics with daily deaths and hospital admissions in London. Methods: Daily concentrations of particle mass (PM10, PM2.5, and PM10-2.5), measured using gravimetric, tapered-element-oscillating, and filter-dynamic-measurement-system samplers, as well as particle number concentration and particle composition (carbon, sulfate, nitrate and chloride), were collected from a background monitoring station in central London between 2000 and 2005. All-cause and cause-specific deaths and emergency admissions to hospital in London for the same period were also collected. A Poisson regression time-series model was used in the analysis. Results: The results were not consistent across the various outcomes and lags. Particle number concentration was associated with daily mortality and admissions, particularly for cardiovascular diseases lagged 1-day; increases in particle number concentration (10,166 n/cm³) were associated with 2.2% (95% confidence interval = 0.6% to 3.8%) and 0.6% (-0.4% to 1.7%) increases in cardiovascular deaths and admissions, respectively. Secondary pollutants, especially nonprimary PM, nitrate and sulfate, were more important for respiratory outcomes. Conclusions: This study provides some evidence that specific components of the particle mixture for air pollutants may be relevant to specific diseases. Interpretation should be cautious, however, in particular because exposures were based upon data from a single centrally located monitoring site. There is a need for replication with more comprehensive exposure data, both in London and elsewhere.
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