Evaluating real-world emissions from in-use buses and taxis using on-road remote sensing.

Assessing real-world emissions from buses and taxis is vital to comprehend their impact on urban air quality. Such vehicles differ significantly from the majority of the fleet owing to their higher mileage rates. However, few studies have focused on specifically assessing the emissions from this segment of the vehicle fleet. In this context, this study evaluated the real-world emissions of nitrogen oxides (NOx) from in-use buses and taxis in Dublin, Ireland, using crossroad remote sensing technology. The remote sensing system was deployed at strategic locations throughout the city to capture on-road emissions from passing vehicles. The collected data included vehicle related information such as emission standard, make, and mileage, and pollutants including NOx. Based on this data, analysis was aimed to understand the impact of Euro emission standard, ambient temperature, mileage, and make of the vehicle on NOx emissions. The results reveal that the average emissions from taxis reduce by 37% from Euro 5 to Euro 6b, and average emissions from Euro 6 buses are 87% lower compared to Euro 5. The trends in emission factors (EFs) of buses and taxis were similar during summer and winter sampling. Moreover, on comparing the emissions from the top five taxi manufacturers, different trends in the emission factors were observed. Finally, the study found that the effect of vehicle mileage on emissions was unclear for both buses and taxis. In any case, these findings provide valuable insights into the real-world emission performance of the existing fleet of buses and taxis in Dublin and highlight the need for targeted measures to reduce emissions from these vehicles. The results can assist policymakers and urban planners in formulating evidence-based strategies to improve air quality in Dublin and other cities facing similar challenges.

A trait-based investigation into evergreen woody plants for traffic-related air pollution mitigation over time.

This study investigated influences of leaf traits on particulate matter (PM) wash-off and (re)capture (i.e., net removal) over time. Leaf samples were taken before and after three rainfall events from a range of 10 evergreen woody plants (including five different leaf types), which were positioned with an optical particle counter alongside a busy road. Scanning electron microscopy was used to quantify the density (no./mm2), mass (µg/cm2), and elemental composition of deposited particles. To enable leaf area comparison between scale-like leaves and other leaf types, a novel metric (FSA: foliage surface area per unit branch length) was developed, which may be utilised by future research. Vehicle-related particles constituted 15 % of total deposition, and there was a notable 50 % decrease in the proportion of tyre wear particles after rainfall. T. baccata presented the lowest proportion (11.1 %) of vehicle-related particle deposition but the most consistent performance in terms of net PM removal. Only four of the 10 plant specimens (C. japonica, C. lawsoniana, J. chinensis, and T. baccata) presented effective PM wash-off across all particle size fractions and rainfall intensities, with a generally positive relationship observed between rainfall intensity and wash-off. Mass deposition was more significantly determined by particle size than number density. Interestingly, larger particles were also less easily washed off than smaller particles. Some traits typically considered to be advantageous (e.g., greater hairiness) may in fact hinder net removal over time due to retention under rainfall. Small leaf area is one trait that may promote both accumulation and wash-off. However, FSA was found to be the most influential trait, with an inverse relationship between FSA and wash-off efficacy. This finding poses trade-offs and opportunities for green infrastructure design, which are discussed. Finally, numerous areas for future research are recommended, underlining the importance of systems approaches in developing vegetation management frameworks.

In-kitchen aerosol exposure in twelve cities across the globe.

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with âˆ¼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185 ± 48 (220 ± 58) µg m-3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) µg m-3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m3; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.

Numerical investigation on the ingress of particulate matter from ambient air into the inlet of a building air handling unit.

The transportation of ambient particulate matter (PM) from outdoor air into the inlet of a mechanical building ventilation system is poorly understood. No studies have examined the effect commonly used commercial air handling unit (AHU) inlet designs have upon the migration of PM from the ambient environment into the building ventilation system, and implications of this on energy consumption and indoor air quality (IAQ). Through the numerical analysis of commercial AHU inlets, the differences in concentration of PM in ambient air and that within AHUs were determined, more commonly referred to as Aspiration Efficiency (AE %). A 20-50% difference in particle concentrations between ambient air and the in-AHU concentration was observed between forward and rear-facing AHUs relative to ambient wind direction and speed, and at the maximum ventilation flow rate. Furthermore, a decrease in the ventilation flow rates resulted in a significant reduction in PM concentrations entering the rear-facing AHU. Increasing the Stoke number led to lower AE as a continuous decrease was observed for both rear-facing inlets. The findings of this paper show that AHU inlet design has significant implications on IAQ and building energy consumption, and scope exists to design these inlets to impact both aspects positively.

The nexus between air pollution, green infrastructure and human health.

Cities are constantly evolving and so are the living conditions within and between them. Rapid urbanization and the ever-growing need for housing have turned large areas of many cities into concrete landscapes that lack greenery. Green infrastructure can support human health, provide socio-economic and environmental benefits, and bring color to an otherwise grey urban landscape. Sometimes, benefits come with downsides in relation to its impact on air quality and human health, requiring suitable data and guidelines to implement effective greening strategies. Air pollution and human health, as well as green infrastructure and human health, are often studied together. Linking green infrastructure with air quality and human health together is a unique aspect of this article. A holistic understanding of these links is key to enabling policymakers and urban planners to make informed decisions. By critically evaluating the link between green infrastructure and human health via air pollution mitigation, we also discuss if our existing understanding of such interventions is sufficient to inform their uptake in practice. Natural science and epidemiology approach the topic of green infrastructure and human health very differently. The pathways linking health benefits to pollution reduction by urban vegetation remain unclear and the mode of green infrastructure deployment is critical to avoid unintended consequences. Strategic deployment of green infrastructure may reduce downwind pollution exposure. However, the development of bespoke design guidelines is vital to promote and optimize greening benefits, and measuring green infrastructure's socio-economic and health benefits are key for their uptake. Greening cities to mitigate pollution effects is on the rise and these need to be matched by scientific evidence and appropriate guidelines. We conclude that urban vegetation can facilitate broad health benefits, but there is little empirical evidence linking these benefits to air pollution reduction by urban vegetation, and appreciable efforts are needed to establish the underlying policies, design and engineering guidelines governing its deployment.

Assessing the Impact of Vehicle Speed Limits and Fleet Composition on Air Quality Near a School.

Traffic is a major source of urban air pollution that affects health, especially among children. As lower speed limits are commonly applied near schools in many cities, and different governments have different policies on vehicle fleet composition, this research estimated how different speed limits and fleet emissions affect air quality near a primary school. Based on data of traffic, weather, and background air quality records in Dublin from 2013, traffic, emission, and dispersion models were developed to assess the impact of different speed limits and fleet composition changes against current conditions. Outside the school, hypothetical speed limit changes from 30 km/h to 50 km/h could reduce the concentration of NO2 and PM10 by 3% and 2%; shifts in the fleet from diesel to petrol vehicles could reduce these pollutants by 4% and 3% but would increase the traffic-induced concentrations of CO and Benzene by 63% and 35%. These changes had significantly larger impacts on air quality on streets with higher pollutant concentrations. Findings suggest that both road safety and air quality should be considered when determining speed limits. Furthermore, fleet composition has different impacts on different pollutants and there are no clear benefits associated with incentivising either diesel or petrol engine vehicles.

"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution.

Air pollution is now recognized as the world's single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed "Active Population Exposure" was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to "Home Population Exposure", which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the "Active Population Exposure" scenario. Population-weighted exposure computed in each district of NYC for the "Active" scenario were found to be statistically significantly (p < 0.05) different to the "Home" scenario for most districts. In investigating the temporal variability of the "Active" population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

UNLABELLED: Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 µm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. IMPLICATIONS: The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

Current and Future Environmental Balance of Small-Scale Run-of-River Hydropower.

Globally, the hydropower (HP) sector has significant potential to increase its capacity by 2050. This study quantifies the energy and resource demands of small-scale HP projects and presents methods to reduce associated environmental impacts based on potential growth in the sector. The environmental burdens of three (50-650 kW) run-of-river HP projects were calculated using life cycle assessment (LCA). The global warming potential (GWP) for the projects to generate electricity ranged from 5.5-8.9 g CO2 eq/kWh, compared with 403 g CO2 eq/kWh for UK marginal grid electricity. A sensitivity analysis accounted for alternative manufacturing processes, transportation, ecodesign considerations, and extended project lifespan. These findings were extrapolated for technically viable HP sites in Europe, with the potential to generate 7.35 TWh and offset over 2.96 Mt of CO2 from grid electricity per annum. Incorporation of ecodesign could provide resource savings for these HP projects: avoiding 800 000 tonnes of concrete, 10 000 tonnes of steel, and 65 million vehicle miles. Small additional material and energy contributions can double a HP system lifespan, providing 39-47% reductions for all environmental impact categories. In a world of finite resources, this paper highlights the importance of HP as a resource-efficient, renewable energy system.

Comparison of particulate matter dose and acute heart rate variability response in cyclists, pedestrians, bus and train passengers.

Exposure to airborne particulate matter (PM) has been linked to cardiovascular morbidity and mortality. Heart rate variability (HRV) is a measure of the change in cardiac autonomic function, and consistent links between PM exposure and decreased HRV have been documented in studies. This study quantitatively assesses the acute relative variation of HRV with predicted PM dose in the lungs of commuters. Personal PM exposure, HR and HRV were monitored in 32 young healthy cyclists, pedestrians, bus and train passengers. Inhaled and lung deposited PM doses were determined using a numerical model of the human respiratory tract which accounted for varying ventilation rates between subjects and during commutes. Linear mixed models were used to examine air pollution dose and HRV response relationships in 122 commutes sampled. Elevated PM2.5 and PM10 inhaled and lung deposited doses were significantly (p<0.05) associated with decreased HRV indices. Percent declines in SDNN (standard deviation of normal RR intervals) relative to resting, due to an inter-quartile range increase in PM10 lung deposited dose were stronger in cyclists (-6.4%, 95% CI: -11.7, -1.3) and pedestrians (-5.8%, 95% CI: -11.3, -0.5), in comparison to bus (-3.2%, 95% CI: -6.4, -0.1) and train (-1.8%, -7.5, 3.8) passengers. A similar trend was observed in the case of PM2.5 lung deposited dose and results for rMSSD (the square root of the squared differences of successive normal RR intervals) followed similar trends to SDNN. Inhaled and lung deposited doses accounting for varying ventilation rates between modes, individuals and during commutes have been neglected in other studies relating PM to HRV. The findings here indicate that exercise whilst commuting has an influence on inhaled PM and PM lung deposited dose, and these were significantly associated with acute declines in HRV, especially in pedestrians and cyclists.

A principal components analysis of the factors effecting personal exposure to air pollution in urban commuters in Dublin, Ireland.

Principal component analysis was used to examine air pollution personal exposure data of four urban commuter transport modes for their interrelationships between pollutants and relationships with traffic and meteorological data. Air quality samples of PM2.5 and VOCs were recorded during peak traffic congestion for the car, bus, cyclist and pedestrian between January 2005 and June 2006 on a busy route in Dublin, Ireland. In total, 200 personal exposure samples were recorded each comprising 17 variables describing the personal exposure concentrations, meteorological conditions and traffic conditions. The data reduction technique, principal component analysis (PCA), was used to create weighted linear combinations of the data and these were subsequently examined for interrelationships between the many variables recorded. The results of the PCA found that personal exposure concentrations in non-motorised forms of transport were influenced to a higher degree by wind speed, whereas personal exposure concentrations in motorised forms of transport were influenced to a higher degree by traffic congestion. The findings of the investigation show that the most effective mechanisms of personal exposure reduction differ between motorised and non-motorised modes of commuter transport.

The control of environmental tobacco smoke: a policy review.

According to World Health Organisation figures, 30% of all cancer deaths, 20% of all coronary heart diseases and strokes and 80% of all chronic obstructive pulmonary disease are caused by cigarette smoking. Environmental Tobacco Smoke (ETS) exposure has also been shown to be associated with disease and premature death in non-smokers. In response to this environmental health issue, several countries have brought about a smoking ban policy in public places and in the workplace. Countries such as the U.S., France, Italy, Ireland, Malta, the Netherlands, Sweden, Scotland, Spain, and England have all introduced policies aimed at reducing the population exposure to ETS. Several investigations have monitored the effectiveness of these smoking ban policies in terms of ETS concentrations, human health and smoking prevalence, while others have also investigated a number of alternatives to smoking ban policy measures. This paper reviews the state of the art in research, carried out in the field of ETS, smoking bans and Tobacco Control to date and highlights the need for future research in the area.

Optimal cycling and walking speed for minimum absorption of traffic emissions in the lungs.

This study investigated whether, over a number of fixed distances and under a number of different exposure concentrations, the cyclist will adsorb less benzene by cycling/walking slowly with a relatively low breathing rate or by cycling/walking as fast as reasonably possible with a relatively high breathing rate. Breathing rates were measured in the laboratory for various cycling/walking speeds over set distances. These breathing rates could then be entered into a numerical model of the human respiratory tract together with the journey times and pollutant concentrations to assess the total absorption of pollutants in the lungs. Results show that cycling and walking at a relatively fast speed and therefore breathing at a higher rate over a shorter duration of exposure results in lower total absorption of benzene than cycling/walking the same distance at a slower speed. The magnitude of this reduction (was more notable at lower concentrations than at high concentrations in the alveolar region of the lungs and was more notable for the pedestrian than cyclist. The cycling at a faster pace resulted in a 17% reduction in total absorption, while walking at a faster pace resulted in a 26% reduction compared to travelling at a slower pace in both cases.

Effects of the smoking ban on benzene and 1,3-butadiene levels in pubs in Dublin.

According to World Health Organisation figures, 30% of all cancer deaths, 20% of all coronary heart diseases and strokes and 80% of all chronic obstructive pulmonary disease are caused by cigarette smoking. In accordance with the recommendations of the Tobacco Free Policy Review Group Report the Irish government has introduced a smoking ban in all workplaces with the exception of prisons and psychiatric hospitals. This study measured the levels of benzene and 1,3-butadiene in air, two known carcinogens and environmental tobacco smoke (ETS) markers, in pubs both before and after the smoking ban was introduced. The results of the study have quantified the significant gross differences in pre and post ban exposure levels. The International Commission on Radiological Protection (ICRP), Human Respiratory Tract model for Radiological Protection has then been adopted to assess the amounts of these pollutants typically absorbed in the nose, throat and lungs of the workers and patrons of pubs in Ireland before and after the smoking ban. This has revealed a reduction in the average dose of benzene and 1,3-butadiene of 91% and 95% respectively for a typical three hour exposure in Irish pub.