Deterioration Of Urban Air Quality Research Articles

Innovating for Cleaner Skies: A Study on the Impact of China's National Innovation Demonstration Zones on Urban Air Quality from the Perspective of Energy Consumption

In pursuit of innovation-driven green development, effectively leveraging innovation policies for environmental betterment has emerged as a pivotal challenge in China's sustainable development trajectory. Investigating 287 Chinese cities from 2010 to 2020, this study adopts the pilot initiative of National Innovation Demonstration Zones (NIDZs), a cornerstone of China's regional innovation strategy, as a quasi-natural experimental setup. Utilizing a double machine learning method, we probe into the implications of implementation National Innovation Demonstration Zones pilot policy on urban air quality, its underlying mechanisms, and the regional heterogeneity in policy outcomes. The empirical evidence underscores that implementation National Innovation Demonstration Zones pilot policy significantly bolster urban air quality, a result that persists endogenous controls and rigorous robustness checks. Mechanistically, setting up the National Innovation Demonstration Zone can significantly curb the deterioration of urban air quality by reducing the total energy consumption of the whole society, including the reduction of total electricity consumption, the use of manufactured gas and natural gas, and the consumption of liquefied petroleum gas. Regionally, the Southeast demonstrates enhanced efficacy in mitigating air quality degradation and PM2.5 concentration, whereas the Northwest sees pronounced reductions in urban carbon dioxide emissions following the implementation National Innovation Demonstration Zones pilot policy.

Impact of urbanization on fine particulate matter concentrations over central Europe

Abstract. Rural-to-urban transformation (RUT) is the process of turning a rural or natural land surface into an urban one, which brings about important modifications in the surface, causing well-known effects like the urban heat island (UHI), reduced wind speeds, and increased boundary layer heights. Moreover, with concentrated human activities, RUT introduces new emission sources which greatly perturb local and regional air pollution. Particulate matter (PM) is one of the key pollutants responsible for the deterioration of urban air quality and is still a major issue in European cities, with frequent exceedances of limit values. Here we introduce a regional chemistry–climate model (regional climate model RegCM coupled offline to chemistry transport model CAMx) study which quantifies how the process of RUT modified the PM concentrations over central Europe including the underlying controlling mechanisms that contribute to the final PM pollution. Apart from the two most studied ones, (i) urban emissions and (ii) urban canopy meteorological forcing (UCMF; i.e. the impact of modified meteorological conditions on air quality), we also analyse two less studied contributors to RUT's impact on air quality: (iii) the impact of modified dry-deposition velocities (DVs) due to urbanized land use and (iv) the impact of modified biogenic emissions due to urbanization-induced vegetation modifications and changes in meteorological conditions which affect these emissions. To calculate the magnitude of each of these RUT contributors, we perform a cascade of simulations, whereby each contributor is added one by one to the reference state, while focus is given on PM2.5 (particulate matter with diameter less then 2.5 µm). Its primary and secondary components, namely primary elemental carbon (PEC), sulfates (PSO4), nitrates (PNO3), ammonium (PNH4), and secondary organic aerosol (SOA), are analysed too. The validation using surface measurements showed a systematic negative bias for the total PM2.5, which is probably caused by underestimated organic aerosol and partly by the negative bias in sulfates and elemental carbon. For ammonium and nitrates, the underestimation is limited to the warm season, while for winter, the model tends to overestimate their concentrations. However, in each case, the annual cycle is reasonably captured. We evaluated the RUT impact on PM2.5 over a sample of 19 central European cities and found that the total impact of urbanization is about 2–3 and 1–1.5 µg m−3 in winter and summer, respectively. This is mainly driven by the impact of emissions alone causing a slightly higher impact (1.5–3.5 and 1.2–2 µg m−3 in winter and summer), while the effect of UCMF was a decrease at about 0.2–0.5 µg m−3 (in both seasons), which was mainly controlled by enhanced vertical eddy diffusion, while increases were modelled over rural areas. The transformation of rural land use into an urban one caused an increase in dry-deposition velocities by around 30 %–50 %, which alone resulted in a decrease in PM2.5 by 0.1–0.25 µg m−3 in both seasons. Finally, the impact of biogenic emission modifications due to modified land use and meteorological conditions caused a decrease in summer PM2.5 of about 0.1 µg m−3, while the winter effects were negligible. The total impact of urbanization on aerosol components is modelled to be (values indicate winter and summer averages) 0.4 and 0.3 µg m−3 for PEC, 0.05 and 0.02 µg m−3 for PSO4, 0.1 and 0.08 µg m−3 for PNO3, 0.04 and 0.03 µg m−3 for PNH4, and 0 and 0.05 µg m−3 for SOA. The main contributor of each of these components was the impact of emissions, which was usually larger than the total impact due to the fact that UCMF was counteracted with a decrease. For each aerosol component, the impact of modified DV was a clear decrease in concentration, and finally, the modifications of biogenic emissions impacted SOA predominantly, causing a summer decrease, while a very small secondary effect of secondary inorganic aerosol was modelled too (they increased). In summary, we showed that when analysing the impact of urbanization on PM pollution, apart from the impact of emissions and the urban canopy meteorological forcing, one also has to consider the effect of modified land use and its impact on dry deposition. These were shown to be important in both seasons. For the effect of modified biogenic emissions, our calculations showed that they act on PM2.5 predominantly through SOA modifications, which only turned out to be important during summer.

The Impact of Anthropogenic VOC Emissions on Atmospheric Pollution: A Case Study of a Typical Industrialized Area in China

Volatile organic compounds (VOCs) are the main precursors of pollution from ground ozone (O3) and PM2.5, which cause the deterioration of urban air quality. The emissions of VOCs from industrialized areas are significant and their characteristics are complex, which nowadays contribute significantly to the challenges of investigating the emission inventory. Taking a typical industrialized area in Tianjin as a case study, the anthropogenic VOCs emission inventory for 2020 was established in this study by using the activity data from a large-scale survey and the latest emission factors. The impact of VOCs on the environment was analyzed from the perspective of the combined control of PM2.5 and O3. The results showed that the total emission of VOCs in 2020 was about 1.68 Gg, mainly from industrial processes and mobile sources, which accounted for 38.4% and 36.5% of the total emissions, respectively. The top 10 emitted VOCs were toluene, acetone, ethylbenzene, m/p-xylene, i-pentane, n-hexane, formaldehyde, benzene, ethyl acetate and ethylene. The dominant species of O3 formation potential (OFP) were almost all aromatic hydrocarbons and alkenes, with m/p-xylene contributing the most to the OFP emissions (8.90%). The top 10 secondary organic aerosols formation potential (SOAP) emission species were aromatic hydrocarbons and long-chain alkanes, and the largest emission came from toluene (39.9%). An analysis of an ADMS diffusion model showed that VOCs emitted from traffic-heavy main roads and industrialized central areas had the greatest impact on the air quality in the surrounding areas. The VOCs concentration was higher in winter due to unfavorable meteorological conditions. Our research updated the VOC inventory of industrialized areas and evaluated VOCs species reactivity and their impact on ambient air quality.

Estimating individual vehicle emission factors from near-road measurements in India

Over the years, vehicular emissions have been the main contributor to the deterioration of urban air quality. However, quantification of real-world vehicular emissions is quite limited in low- and middle-income countries like India. Developing real-world vehicle emission factors (EFs) using reference-grade instruments requires a significant amount of resources. This study aims to develop the individual and fleet vehicle EFs and the fraction of high-emitting vehicles using high-time resolution, low-cost sensors from near-road measurements – a first-of-its-kind study in India. Traffic and air pollutant measurements were conducted at the kerbside of a street canyon in Mumbai. The individual vehicle fuel-based EFCO and EFNOx were estimated using the plume-identification technique coupled with the information obtained from the vehicle registration number plates. The fleet mean (±SD) EFCO, EFNO, and EFNO2 were 6.30 (±3.16), 1.38 (±1.17), and 0.43 (±0.32) g/kg, respectively, while for EFPM1, EFPM2.5, EFPM2.5-10, and EFPM10 were 0.70 (±0.34), 1.19 (±0.57), 0.90 (±0.65), and 2.09 (±1.05) g/kg, respectively. The developed individual vehicle EFCO and EFNOx were greatly varied within each vehicle type due to differences in emission control technology, engine size, and the prevalence of “super-emitters”. There was no substantial difference in EFCO and EFNOx among the different BS emission standards across almost all vehicle types. The reconstructed fleet EFCO and EFNOx from the developed single-vehicle EFs were 1.4 and 1.9 times higher than the recorded fleet EFs. Approximately 14% of vehicles in the fleet were identified as super-emitters, responsible for 37–54% of total emissions, primarily from private passenger vehicles such as cars and two-wheelers. The EFCO and EFNOx from these high-emitters were 3–30 times greater than the laboratory-reported emissions. Our study suggests that improving emission standards alone is not enough to decrease tailpipe emissions from vehicles. Proper vehicle inspection and maintenance programs are crucial in controlling these emissions.

A Markov chain-based IoT system for monitoring and analysis of urban air quality.

Severe deterioration of urban air quality in Asian cities is the cause of a large number of deaths every year. A Markov chain-based IoT system is developed in this study to monitor, analyze, and predict urban air quality. The proposed sensing setup is integrated with an automobile and is used for collecting air quality information. An Android application is used to transfer and store the sensed data in the data cloud. The data stored is used to generate the transition matrix of the AQI states and calculate return periods for each AQI state. The estimated time interval after which an AQI event recurs or is repeated is known as return period. The actual return periods for each AQI state at the test locations in Delhi-NCR are compared with those predicted using discrete time Markov chain (DTMC) models. Average absolute forecast error using our model was found to be 3.38% and 4.06%, respectively, at the selected locations.

Research on PM10 diffusion and distribution of moving vehicle in street canyon based on dynamic mesh

Particulate matter (PM) produced by moving vehicles causes the deterioration of urban air quality and seriously endangers the health of residents. By considering PM10 as discrete phase and adopting dynamic mesh updating technology, a mathematical model of PM10 dispersion in three-dimensional ideal street canyon is established to evaluate the contribution of vehicle-induced turbulence to the dispersion of PM10. The dynamic spatial distribution characteristics of PM10 are analyzed when the average vehicle speed is 60 km/h, 50 km/h and 40 km/h. PM10 diffuses outwards in the form of alternating turbulent vortices on both sides, and its diffusion law is mainly affected by vehicle-induced turbulence. After a quarter of total moving time, the peak concentration of PM10 on the horizontal monitoring line drops to 0.3%, 14.4% and 10.7%, respectively, and the peak concentration of PM10 on the vertical monitoring line drops to 0.03%, 9.6% and 4.3%, respectively. For car drivers, they need to maintain a following distance of more than 3.5 m. For roadside pedestrians, they only need to walk on the sidewalk as required. This paper studies the diffusion of PM10 under different moving conditions from microscopic perspective, and provides a theoretical basis for urban development of traffic emission control measures.

Vertical distribution characteristics of particulate matter beside an elevated expressway by unmanned aerial vehicle measurements

Increasing knowledge in the law of particulate matter (PM) distributions beside elevated expressways could assist in addressing the deterioration of urban air quality. However, the vertical distribution patterns of PM concentration have not been clearly recognized due to limited measurement methods. Hence, unmanned aerial vehicle (UAV) measurements are carried out at three typical sites near an elevated expressway to investigate the effects of the noise barrier, vegetation, and building on PM vertical distribution patterns. Results show that the order of mean PM concentration in three trial sites from low to high is: 86.3 μg/m3 at VN (with vegetation and noise barrier), 90.2 μg/m3 at CT (control, without vegetation, noise barrier, and building), and 93.2 μg/m3 at VNB (with vegetation, noise barrier, and building) site. Additionally, the PM1 concentrations present a unimodal distribution, peaking at 5–7 m and 7–9 m at CT and VN sites, respectively. PM10 concentrations decrease continuously with height at CT site and show a bimodal distribution at VN site, with the maximum and the upper peak occurring at 0–3 m and 5–7 m, respectively. High-density vegetation is more conducive to remove PM downwind than low-density vegetation in the open road, while the opposite is true in the street canyon. Moreover, statistically significant positive correlations are obtained between roadside concentration and traffic volume, as well as between roadside concentration and background concentration, with Spearman correlation coefficients of 0.61 and 0.93, respectively. This study could provide insights in mitigating traffic-related PM pollution into urban areas.

An assessment of ambient air quality using AQI and exceedance factor for Udaipur City, Rajasthan (India)

Air pollution is a severe issue of concern worldwide due to its most significant environmental risk to human health today. All substances that appear in excessive amounts in the environment, such as PM10, NO2, or SO2, may be associated with severe health problems. Anthropogenic sources of these pollutants are mainly responsible for the deterioration of urban air quality. These sources include stationary point sources, mobile sources, waste disposal landfills, open burning, and similar others. Due to these pollutants, people are at increased risk of various serious diseases like breathing problems and heart disease, and the death rate due to these diseases can also increase. Hence, air quality monitoring is essential in urban areas to control and regulate the emission of these pollutants to reduce the health impacts on human beings. Udaipur has been selected for the assessment of air quality with monitored air quality data. Air quality monitoring stations in Udaipur city are operated by the CPCB (Central Pollution Control Board) and RSPCB (Rajasthan State Pollution Control Board). The purpose of this study is to characterize the level of urban air pollution through the measurement of PM10, NO2, or SO2 in Udaipur city, Rajasthan (India). Four sampling locations were selected for Udaipur city to assess the effect of urban air pollution and ambient air quality, and it was monitored for a year from 1st January 2019 to 31st December 2019. The air quality index has been calculated with measured values of PM10, NO2, and SO2. The concentration of PM10 is at a critical level of pollution and primarily responsible for bad air quality and high air quality Index in Udaipur city.

An IoT based Sensing System for Modeling and Forecasting Urban Air Quality

Rapid urbanization, vehicular emissions, rise in industrial activities, burning of crop residues and garbage in nearby areas, thermal power plants, emissions from diesel generators, dust from construction sites and household fuel use have been the cause of severe deterioration of urban air quality, resulting in a large number of deaths every year. In this work, an Internet of Things based system has been developed to monitor, analyze and forecast outdoor air quality. Air quality data is collected using our sensing system which is integrated with a vehicle, and collects data while the vehicle moves on the road. The sensed data is transferred and stored in cloud using an Android application. Stored data is used to forecast air quality with the help of statistical and stochastic forecasting models-quantile regression and ARMA/ARIMA. The forecast performance of these prediction models is measured using mean absolute deviation, mean percentage error, mean absolute percentage error, mean square error and root mean square error to find their efficacy.

Changes in Air Quality during the First-Level Response to the Covid-19 Pandemic in Shanghai Municipality, China

Ongoing rapid urban population growth world-wide has led to serious environmental problems that affect ecosystems and also lower the security and happiness of urban residents about their living environment. The most frequently reported negative impact is a deterioration in urban air quality. In this study, we performed a comprehensive assessment of the effects of the city lockdown policy in response to Covid-19 on air quality in Shanghai Municipality, China, and sought to identify a balance point between human activities and improving air quality. The first-level response (FLR) by Shanghai to control the spread of Covid-19 was to launch a lockdown, which remained in place from 24 January to 23 March, 2020. We compared airborne pollutant concentrations in different regions (downtown, suburbs) of Shanghai city in three periods (Pre-FLR, During-FLR, and Post-FLR) and in the corresponding periods in the previous year. The results showed that air quality improved significantly During-FLR compared with Pre-FLR, with the concentrations of PM2.5, PM10, SO2, NO2, and CO all decreasing significantly. The concentrations of all pollutants except O3 also decreased significantly compared with the same period in the previous year. There were also some differences in pollutant concentrations between the downtown region and the suburbs of Shanghai. However, we found that the concentrations of pollutants rebounded gradually when the restrictions on human activities ended after two months of lockdown. This study provides empirical evidence of the important effect of limiting human activities on air quality. For sustainable and clean future urban management in Shanghai and beyond, central government policy regulations requiring a low-carbon lifestyle and cleaner production in industries should be established.

On-board measurement of emissions from freight trucks in urban arterials: Effect of operating conditions, emission standards, and truck size

Diesel trucks contribute significantly to emissions leading to deterioration in urban air quality. Many recent studies have attempted to quantify real-world emissions from trucks in cities. However, studies on real-world emission from trucks in India are scarce. In this study, we measured real-world emissions of gaseous pollutants (CO, HC, and NO) from twenty trucks of different sizes and emission standards. The trucks were classified into three categories (light-duty diesel trucks (LDDTs), medium-duty diesel trucks (MDDTs), and heavy-duty diesel trucks (HDDTs)) based on their gross vehicle weight (GVW). The tested trucks conformed to Bharat Stage (BS) II, BS III, and BS IV emission standards. A vehicle specific power (VSP) based approach was adopted to model the relationship between vehicle activity and emissions. In addition, real-world emission factors of CO, HC, and NO for all the truck types were determined and compared based on their emission standard. Considerable reductions in emission factors were observed from lower to higher emission control standards for all the pollutants. The CO, HC and NO emission factors from the BS II standard LDDTs were 8.05 g/km, 0.86 g/km, and 8.04 g/km, respectively. The CO emission factor decreased by 29% and 65% respectively, for LDDTs and MDDTs with improved emission standards. The HC emissions (g/km) reduction from BS II to BS III for LDDTs and HDDTs were 49% and 8%, respectively. In the case of MDDTs, HC emission factor reduced by 16% from BS II to BS IV. On comparing with ARAI emission standards, we found that the emission factors of the trucks with GVW less than 3500 kg were higher for all the pollutants. In the case of trucks with GVW greater than 3500 kg, the CO emission factor for BS II and BS III complying trucks were higher than standard values while HC emission factors were lower than the standard values. Further, the emission factors of CO and NO from BS IV standard trucks were higher than the standard values. Moreover, emission factor values from our study were higher than that reported in the literature. Although India aims to adopt BS VI emission standards by 2020, phasing out older vehicles will be the key to mitigate emissions from diesel vehicles.

Spatial estimation of urban air pollution with the use of artificial neural network models

The deterioration of urban air quality is considered worldwide one of the primary environmental issues and scientific evidence associates the exposure to ambient air pollution with serious health effects. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. Interpolation methods for point estimations in the field of air pollution modelling enable the estimation of pollutant concentrations in unmonitored locations. The main objective of this study is to evaluate two interpolation methodologies, Artificial Neural Networks and Multiple Linear Regression, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece. The results for five regulated air pollutants (Nitrogen dioxide, Nitrogen monoxide, Ozone, Carbon monoxide and Sulphur dioxide) are compared through the use of a set of correlation and difference statistical measures and residuals distribution. Artificial neural networks are found in most cases to be significantly superior, especially where the air quality network density is limited, leading to a decreased degree of spatial correlations among the monitoring sites.

Bioaccessibility of Airborne Particulate-Bound Trace Elements in Shanghai and Health Risk Assessment

Increased concentrations of airborne particulate matter in megacities within China, caused by rapid economic growth, urbanization and industrialization, have become a major health concern. High energy consumption, emissions from a large fleet of on-road vehicles and intense industrial activities all contributed to the deterioration of urban air quality in China. In this study, we investigated the bioaccessibility of twelve particulate-bound trace elements (Al, Co, Cd, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, and Zn) using simulated lung fluids (Gamble’s solution (pH = 7.4) and artificial lysosomal fluid (ALF) (pH = 4.5)) following the collection of PM in Shanghai, a megacity in China, to gain a better understanding of their fate upon inhalation by adults and children. All the trace elements showed higher bioaccessibility in ALF, compared to that in the Gamble’s solution, due to lower pH and higher ionic strength of the former over the latter. In most cases, the kinetic response of trace elements in terms of their solubility followed a curvilinear pattern and approached near steady-state characteristics after a protracted period of incubation in the lung fluids. Health risk assessment was conducted for adults and children based on the measured bioaccessible fractions of trace elements, which showed significant excess lifetime cancer risk to both children and adults.

Increasing impact of urban fine particles (PM2.5) on areas surrounding Chinese cities.

The negative impacts of rapid urbanization in developing countries have led to a deterioration in urban air quality, which brings increasing negative impact to its surrounding areas (e.g. in China). However, to date there has been rare quantitative estimation of the urban air pollution to its surrounding areas in China.We thus evaluated the impact of air pollution on the surrounding environment under rapid urbanization in Chinese prefectures during 1999 – 2011. We found that: (1) the urban environment generated increasing negative impact on the surrounding areas, and the PM2.5 concentration difference between urban and rural areas was particularly high in large cities. (2) Nearly half of the Chinese prefectures (156 out of 350) showed increased impact of urban PM2.5 pollution on its surrounding areas. Those prefectures were mainly located along two belts: one from northeast China to Sichuan province, the other from Shanghai to Guangxi province. Our study demonstrates the deterioration in urban air quality and its potential impacts on its surrounding areas in China. We hope that the results presented here will encourage different approaches to urbanization to mitigate the negative impact caused by urban air pollution, both in China and other rapidly developing countries.

Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden

In the past 20 years, considerable progress has been made to improve urban air quality in the EU. However, road traffic still contributes considerably to the deterioration of urban air quality to below standards, which requires a method to measure properly and model pollution levels resulting from road traffic. In order to visualize the geographical distribution of pollution concentration realistically, we applied the Land Use Regression (LUR) model to the urban area of Gothenburg.The NO2 concentration was already obtained by 25 samplers through the urban area during 7-20 May, 2001. Predictive variables such as altitude, density, roads types, traffic and land use were estimated by geographic information system in buffers ranging 50 to 500 m-radii. Linear regression (α=5%) between NO2 and every predictive variable was calculated, and the most robust variables and without collinearity variables were selected to the multivariate regression model. The final formula was applied using Kriging in a grid map to estimate NO2 levels.The average of measurements was 23.5 μg/m3 (± 6.8 μg/m3) and 180 predictive variables were obtained. The final model explained 59.4% of the variance of NO2 concentration with presence of altitude and sum of traffic within 150 m around the sampler sites as predictor variables. The correlation measured versus predicted levels of NO2 was r = 0.77 (p < 0.001).These results highlight the contribution of traffic in air pollution concentration, although the model is not precise in regions outside the urban area (e.g. islands and rural area). Moreover, future analyses should include meteorological data to improve the LUR modelling.

DEVELOPING CAPACITY AND INFORMATION TOOLS FOR VEHICLE EMISSIONS POLICY IN NEW ZEALAND

Worldwide, the increasing deterioration in urban air quality due to vehicle emissions has created the need for greater capacity in air quality management and more comprehensive knowledge about the sources and spatial and temporal distribution of vehicle emissions, without which impact studies and subsequent mitigation policy cannot be formulated. To investigate this proposition and assist with the design/development of new air quality information tools for this evolving context, this research examines the generation and use of air quality information in New Zealand, through two case studies. The first case study examines NZTER, a tool used for generating 'vehicle fleet emission rates information' used in a number of different models for air quality assessment and transport policy. The second case examines the early stages of the development and operationalisation of an integrated environmental management tool, the VFEM-w, which hopes to quantify/model the type, source and pathways of airborne pollutants entering the urban stormwater water system. While the NZTER is uncomplicated, it is inefficient and produces information of insufficient quality in the current policy-making context. The VFEM-w case illustrates the challenges of attempting to embed a conceptually and technically complex model in an existing cultural, technical and political setting. Recommendations for the use and development of air quality information tools are made and closing comments consider the issue of information quality in sustainable management.

Semi-volatile and particulate emissions from the combustion of alternative diesel fuels

Motor vehicle emissions are a major anthropogenic source of air pollution and contribute to the deterioration of urban air quality. In this paper, we report results of a laboratory investigation of particle formation from four different alternative diesel fuels, namely, compressed natural gas (CNG), dimethyl ether (DME), biodiesel, and diesel, under fuel-rich conditions in the temperature range of 800–1200°C at pressures of approximately 24 atm. A single pulse shock tube was used to simulate compression ignition (CI) combustion conditions. Gaseous fuels (CNG and DME) were exposed premixed in air while liquid fuels (diesel and biodiesel) were injected using a high-pressure liquid injector. The results of surface analysis using a scanning electron microscope showed that the particles formed from combustion of all four of the above-mentioned fuels had a mean diameter less than 0.1 μm . From results of gravimetric analysis and fuel injection size it was found that under the test conditions described above the relative particulate yields from CNG, DME, biodiesel, and diesel were 0.30%. 0.026%, 0.52%, and 0.51%, respectively. Chemical analysis of particles showed that DME combustion particles had the highest soluble organic fraction (SOF) at 71%, followed by biodiesel (66%), CNG (38%) and diesel (20%). This illustrates that in case of both gaseous and liquid fuels, oxygenated fuels have a higher SOF than non-oxygenated fuels.

Planet Earth and automobiles, with special reference to the Indian automobile industry

This paper deals with the deterioration of urban air quality due to pollution from automobiles. The paper highlights the magnitude of the problem and how it can be controlled at the manufacturing level, if proper action is initiated now. It is a techno-managerial issue, but managerial issues are getting little attention while the technology advances in the right direction (to some extent). This lack of emphasis on managerial issues has resulted in an unawareness of the dangers of cars polluting our environment. The breakthrough in technology is not being managed to achieve maximum impact for saving the Earth's already fragile environment. Apart from this, there are also possibilities that exist outside the automobile industry; such as better traffic management systems to keep the traffic moving and avoiding congestion.

Global Emissions Due To Urban Transport And The Potential For Their Reduction

The worldwide growth in the volume of transportation is giving rise to increasing concern over the consequent increase in emissions. The rise in carbon oxide emissions is hindering the drive to minimize the 'greenhouse effect' and other more serious emissions are causing a deterioration in urban air quality. Urban transportation is estimated to account for over 20% of total carbon monoxide emissions in the United States, of which up to 50% is due to energy wasted in braking. The figure worldwide could well be approaching this. Assuming that 60% of this energy is recoverable through regenerative braking, it is possible that total global carbon monoxide emissions could be reduced by 4% by implementation of currently available brake energy recovery technologies. The reduction in other emissions could be far more significant due to the optimized engine management systems which such technologies allow. This paper describes brake energy recovery technologies, and their introduction so far, in comparison with other measures such as use of alternative fuels.

Congestion, air pollution, and road fatalities in urban areas

The continuous rapid growth in vehicle miles travelled coupled with the rapid increase in traffic congestion on highways of virtually every large urban area, explain a major portion of the observed deterioration of urban air quality. To halt this deterioration and to secure safe and healthy environments and improve the quality of life in our cities, it is useful to initiate and implement programs that treat jointly traffic congestion, air quality, and road safety. Market-based strategies, driven by price mechanisms, have been proposed as the best and most efficient way to decrease traffic congestion and to reduce vehicle emission. Congestion pricing, emission fees, reducing emissions of high-polluting vehicles, and introducing more efficient vehicle and/or fuel technologies are not mutually exclusive strategies and therefore they can be employed jointly within an overall strategy. In view of the conflicting objectives that may exist between improving urban air quality and reducing road fatalities and traffic congestion, it is of great importance to investigate thoroughly these functional relationships. The results of such studies will help decision makers identify the “socially optimal level of congestion” that will yield the highest net social benefit.