Analysis for need of Non-Motorised Transport (NMT) facilities in Khamgaon city

In our day to day life we have seen the emission from transport sector is increasing. Along with carbon emissions, the transport sector is also responsible for road congestion, local air pollution, noise and accidents. In urban areas, the share of both public transport and NMT has been decreasing, resulting in increasing negative impacts. Therefore development of NMT in the cities is very important. NMT comprises of walking, Cycling, wheel chair travel and small wheeled transport etc. This transport facilities fulfill transportation as well as recreational objectives as these are ecofriendly modes. Previous planning only focus on movement of MT and there is no planning for NMT but the scenario is most of the short trips upto 5km in our country are carried out by walking and cycling.. NMT promotes health as well as social equality and is free from pollution which makes it ecofriendly. NMT are affordable to all classes which maintains social equality among citizens. Present Indian scenario shows undermining of importance and safety of NMVs in Indian cities. In India it should be taken seriously to priorities the use of NMT and focus on implementation of this facilities, maintenance and operation of this system. The governments should run the programs to encourage the people to move towards the use of NMT. This study aims to determine the need of NMT facilities within the city and people views regarding existing traffic conditions along main streets within the city. The city is divided in different parts as per wards and quessionnaire survey is carried out to know people views. The overall study shows the need for the provision of NMT facilities like footpaths, cycle ways, croos points at main streets within the city. Keywords: NMT- Non Motorised Transport, NMV- Non Motorised Vehicles, MT- Motorised Transport

Determinants of Electric Vehicle Diffusion in China

We analyze the effect of four determinants of electric vehicle diffusion in China for a panel of 31 regions for the period 2010–2016. We analyze diffusion of four different electric vehicle types, namely battery electric cars and buses as well as plug-in hybrid electric cars and buses. System GMM panel estimation results show that total monetary subsidies have a positive effect only on the diffusion of battery electric cars. A closer look reveals that subsidies provided by regional governments are decisive for all types of vehicles but the subsidy provided by the central government and its degression over time dilute the overall effect of subsidies and is partly detrimental. Non-monetary ownership policies, such as license-plate lotteries, show a positive effect only for battery electric cars. Availability of public charging infrastructure increases diffusion of all vehicle types. Charging points are relevant for cars, while charging stations are especially decisive for the diffusion of electric buses. Using local environmental conditions as a novel determinant for the diffusion of electric vehicles reveals that the local air pollution influences the diffusion of buses, but not of cars.

Evaluating uncertainty in sensor networks for urban air pollution insights

Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCS) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCS has been tested in laboratory and field environments, and a growing literature on uses of LCS primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network’s ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCS across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ~35 % average uncertainty (root-mean-square error) of the calibrated LCS, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London’s air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network’s ability to generate robust information about London’s air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing and interpreting data.

Difference Equation Model-Based PM 2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China

Accurate reporting and prediction of PM 2.5 concentration are very important for improving public health. In this article, we use a spectral clustering algorithm to cluster 44 cities in the Bohai Rim Region. On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM 2.5 propagation between and within clusters for real-time prediction. For example, through the analysis of PM 2.5 concentration data for 92 consecutive days in the Bohai Rim Region, and according to different accuracy definitions, the average prediction accuracy of the difference equation model in all city clusters is 97% or 90%. The mean absolute error (MAE) of the forecast data for each urban agglomeration is within 7 units μg / m 3 . The experimental results show that the difference equation model can effectively reduce the prediction time, improve the prediction accuracy, and provide decision support for local air pollution early warning and urban comprehensive management.

Contribution of Local and Transboundary Air Pollution to the Urban Air Quality of Fukuoka, Japan

Transboundary air pollution (TAP) and local air pollution (LAP) influence the air quality of urban areas. Fukuoka, located on the west side of Japan and affected by TAP from the Asian continent, is a unique example for understanding the contribution of LAP and TAP. Gaseous species and particulate matter (PM) were measured for approximately three weeks in Fukuoka in the winter of 2018. We classified two distinctive periods, LAP and TAP, based on wind speed. The classification was supported by variations in the concentration of gaseous species and by backward trajectories. Most air pollutants, including NOx and PM, were high in the LAP period and low in the TAP period. However, ozone was the exception. Therefore, our findings suggest that reducing local emissions is necessary. Ozone was higher in the TAP period, and the variation in ozone concentration was relatively small, indicating that ozone was produced outside of the city and transported to Fukuoka. Thus, air pollutants must also be reduced at a regional scale, including in China.

Expanding access to electric vehicles in California’s low-income communities

In September 2020, California Governor Gavin Newsom announced an ambitious executive order requiring all new passenger vehicles sold in the state to be zero-emission by 2035 (California Governor 2020). This action recognizes that the growing threats of climate change and local air pollution necessitate a rapid transition away from traditional internal combustion engine vehicles. In California, disadvantaged populations in particular face significant barriers, including financing and charging, to electric vehicle (EV) adoption. For the state to meet its clean energy goals, it must include these communities in a more just transition to widespread use of EVs. This should include avenues of support such as updating state building codes for multi-unit dwellings, where almost half of all Californians live. Charging site access must be increased through curbside infrastructure expansion and institution of statewide EV charger rebates. Finally, increased access to vehicles themselves should be facilitated by expanding incentives and formats for EV sharing programs.

Are Electric Vehicle Subsidies Becoming More Impactful Over Time?

Various subsidies for plug-in electric vehicles (PEVs) have been implemented worldwide at the federal, state and regional levels. These subsidies aim to promote PEV adoption to help reduce both local air pollution and greenhouse gas emissions (Hardman 2019). In the United States (U.S.), the federal government began subsidizing PEVs in 2010.

Hedonic housing prices and environmental quality in Lebanon

Purpose This study aims to examine the impact of local air pollution on housing prices in Lebanon. Design/methodology/approach The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification. Findings The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis. Research limitations/implications Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model. Practical implications The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers. Social implications The findings suggest that pollution is a significant factor in household behavior in Lebanon. Originality/value This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.