Job accessibility and environmental quality are rarely equally distributed in spatial and/or social dimensions within metropolitan regions. Availability of these affects the quality of residential locations, and can be expected to be capitalised into house prices. For prospective house owners, their options will be limited to sub housing markets within certain price bands depending on their available housing budgets. Availability and marginal prices of job accessibility and environmental quality, as well as trade-offs between them, might be different between these submarkets. Using Greater London as the case metropolitan region, this study explored such differences, to shed light on the role of housing market in equity and/or inequity in job accessibility, environmental quality and their interactions. Results of this study show that lower-price submarkets have advantages in job accessibility in terms of marginal price, but are disadvantaged in terms of availability. Differences are more mixed in marginal price and availability between the submarkets for environmental quality. When balancing job accessibility and environmental quality within constrained housing budgets, households in lower-price submarkets would find it relatively easier to gain job accessibility with less sacrifice on environmental quality as compared to those searching in higher-price submarkets, but hard to reach the higher levels of job accessibility that are mainly reserved for the higher-price submarkets.
Cyclists are exposed to direct traffic emissions due to their proximity to on-road vehicles. Several studies associate black carbon (BC) exposure with both mortality and morbidity caused by cardiovascular and respiratory diseases. We did a comparative assessment of cyclists' exposure to BC in three cities: London, Rotterdam and São Paulo. We measured personal exposure to BC during the peak and off-peak hours in all three cities using the same instrument. Three origin-destination (O-D) pairs, each with two routes, for a total of six routes, were chosen in each city. The first route of each O-D pair was along busy major roads and the other perceived to be clean passing close to green/blue/quiet areas. This work brings together results from three different Latin American and European cities, with an aim to understand the BC exposure variabilities while cycling during peak and off-peak hours, identify main pollution hotspots resulting in enhanced exposure and associate the measured concentrations with proximity to green areas and waterways. BC concentrations were higher during the morning-peak hours compared with evening-peak hours in Rotterdam and São Paulo. London showed an opposite trend, with higher concentrations during evening hours. In most cases, the cyclists using the alternative route were found to be less exposed to BC in London and São Paulo. In Rotterdam, the differences in absolute concentrations between main and alternate routes were modest. Each city is different but the common features among all were that the exposure is related to route choice, a period of the day and proximity with the mobile sources. These findings have implications in terms of considering the pollutants exposure when establishing new cycle routes.
We investigated the determinants of personal exposure concentrations of commuters’ to black carbon (BC), ultrafine particle number concentrations (PNC), and particulate matter (PM1, PM2.5 and PM10) in different travel modes. We quantified the contribution of key factors that explain the variation of the previous pollutants in four commuting routes in London, each covered by four transport modes (car, bus, walk and underground). Models were performed for each pollutant, separately to assess the effect of meteorology (wind speed) or ambient concentrations (with either high spatial or temporal resolution). Concentration variations were mainly explained by wind speed or ambient concentrations and to a lesser extent by route and period of the day. In multivariate models with wind speed, the wind speed was the common significant predictor for all the pollutants in the above-ground modes (i.e., car, bus, walk); and the only predictor variable for the PM fractions. Wind speed had the strongest effect on PM during the bus trips, with an increase in 1 m s-1 leading to a decrease in 2.25, 2.90 and 4.98 µg m-3 of PM1, PM2.5 and PM10, respectively. PM2.5 and PM10 concentrations in car trips were better explained by ambient concentrations with high temporal resolution although from a single monitoring station. On the other hand, ambient concentrations with high spatial coverage although lower temporal resolution predicted better the concentrations in bus trips, due to bus routes passing through streets with a high variability of traffic intensity. In the underground models, wind speed was not significant and line and type of windows on the train explained 42% of the variation of PNC and 90% of all PM fractions. Trains in the district line with openable windows had an increase in concentrations of 1684 cm-3 for PNC and 40.69 µg m-3 for PM2.5 compared with trains that has non-openable windows. The results from this work can be used to target efforts to reduce personal exposures of London commuters.
People with low-income often experience higher exposures to air pollutants. We compared the exposure to particulate matter (PM1, PM2.5 and PM10), Black Carbon (BC) and ultrafine particles (PNC; 0.02-1 µm) for typical commutes by car, bus and underground from 4 London areas with different levels of income deprivation (G1 to G4, from most to least deprived). The highest BC and PM concentrations were found in G1 while the highest PNC in G3. Lowest concentrations for all pollutants were observed in G2. We found no systematic relationship between income deprivation and pollutant concentrations, suggesting that differences between transport modes are a stronger influence. The underground showed the highest PM concentrations, followed by buses and a much lower concentrations in cars. BC concentrations in the underground were overestimated due to Fe interference. BC concentrations were also higher in buses than cars because of a lower infiltration of outside pollutants into the car cabin. PNCs were highest in buses, closely followed by cars, but lowest in underground due to the absence of combustion sources. Concentration in the road modes (car and bus) were governed by the traffic conditions (such as traffic flow interruptions) at the specific road section. Exposures were reduced in trains with non-openable windows compared to those with openable windows. People from lower income deprivation areas have a predominant use of car, receiving the lowest doses (RDD<1 µg h-1) during commute but generating the largest emissions per commuter. Conversely, commuters from higher income deprivation areas have a major reliance on the bus, receiving higher exposures (RDD between 1.52-3.49 µg h-1) while generating less emissions per person. These findings suggest an aspect of environmental injustice and a need to incorporate the socioeconomic dimension in life-course exposure assessments.
A megacity typically refers to a metropolitan area with more than 10 million people. The number of megacities worldwide has increased from 8 in 1970 to 34 in 2016 with their total population exceeding 650 million (City Population, 2016). Air pollution, a consequence of increased population and urbanisation, is a common concern in megacities. Here we focus on the Metropolitan Area of São Paulo (MASP), which is the 5th most populous urban region in the world and the second most populated region in Latin America (UN, 2014), making up ~10% of the total population of Brazil. With 21 million inhabitants and 8511 km2 area (Fig. 1a), the MASP includes 38 metropolitan areas surrounding the city of São Paulo that has a population of 12 million (IBGE, 2016). What makes São Paulo distinctly different from all other megacities in the world is that its vehicle fleet operates exclusively on biofuel blends (sugarcane ethanol and soya diesel) in diesel, making it a unique biofuel-driven megacity. Yet, São Paulo’s air quality face challenges to meet its national standards, which are relatively relaxed compared with the megacities of Asia (e.g., Delhi) or Europe (e.g., London). While the events of highly elevated concentrations of particulate matter (PM) are similarly common as in other megacities, the underlining factors responsible for them are unique to São Paulo and the questions are: (i) how can the air quality be improved considering that numerous interventions have already been taken in controlling emissions from vehicular fleet? (ii) how can the transportation system be transformed to make it emission-neutral? (iii) how the emissions from the main emitters such as the diesel trucks and buses can be reduced? and (iv) how the changes in the content of biofuel in diesel have influenced the exceedances and ozone formation? The aim of this paper is to propose answers to the above questions in the context of distinctness in the vehicle fleet, hitherto overlooked sources, underlining causes for pollution exceedances, and to suggest future directions and research needs to better understand and manage air quality of this unique megacity.