Evidence on the Comparison of Telephone and Internet Surveys for Respondent Recruitment

Internet surveys have a potential use for survey research when compared against costs and declining response rates of traditional modes as they form a powerful tool for reducing respondents' burden in complex questionnaires. On the other hand, there exists scepticism about the reliability and robustness of the collected data. Arenze et al. (2005) argue that case studies involving Internet surveys cannot be generalised to other countries and have recommended systematic collection and reporting of experiences worldwide. Such studies have had limited exposure in the transport literature. This paper provides empirical evidence on the comparison between telephone and Internet surveys in the context of a car ownership study. The comparison between telephone and Internet modes focuses on performance measures such as response speed, response rates, survey costs, demographic profiles and geographical representation of the sample. The results indicate the cost effectiveness of Internet surveys. Moreover, they show that the time and cost for data collection significantly vary by sampling and recruitment method. Finally, Internet survey response rates are lower than those in the telephone interview, which implies that Internet surveys can only be used to complement traditional data collection methods

Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants

Background: There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques. Methods: We measured fine particulate matter (PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations. Results: PM2.5 was strongly associated with the central site monitor (R2 = 0.68). Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76). EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52). NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction), and with higher concentrations during summer (R2 = 0.56). Conclusion: Each pollutant examined displayed somewhat different spatial patterns within urban neighborhoods, and were differently related to local traffic and meteorology. Our results indicate a need for multi-pollutant exposure modeling to disentangle causal agents in epidemiological studies, and further investigation of site-specific and meteorological modification of the traffic-concentration relationship in urban neighborhoods

Assessing the distribution of volatile organic compounds using land use regression in Sarnia, "Chemical Valley", Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Land use regression (LUR) modelling is proposed as a promising approach to meet some of the challenges of assessing the intra-urban spatial variability of ambient air pollutants in urban and industrial settings. However, most of the LUR models to date have focused on nitrogen oxides and particulate matter. This study aimed at developing LUR models to predict BTEX (benzene, toluene, ethylbenzene, m/p-xylene and o-xylene) concentrations in Sarnia, 'Chemical Valley', Ontario, and model the intra-urban variability of BTEX compounds in the city for a community health study.</p> <p>Method</p> <p>Using Organic Vapour Monitors, pollutants were monitored at 39 locations across the city of Sarnia for 2 weeks in October 2005. LUR models were developed to generate predictor variables that best estimate BTEX concentrations.</p> <p>Results</p> <p>Industrial area, dwelling counts, and highways adequately explained most of the variability of BTEX concentrations (<it>R</it>²: 0.78 – 0.81). Correlations between measured BTEX compounds were high (> 0.75). Although most of the predictor variables (e.g. land use) were similar in all the models, their individual contributions to the models were different.</p> <p>Conclusion</p> <p>Yielding potentially different health effects than nitrogen oxides and particulate matter, modelling other air pollutants is essential for a better understanding of the link between air pollution and health. The LUR models developed in these analyses will be used for estimating outdoor exposure to BTEX for a larger community health study aimed at examining the determinants of health in Sarnia.</p

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups

<p>Abstract</p> <p>Background</p> <p>Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures.</p> <p>Methods</p> <p>A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994–95 (N ~1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO₂) surface based on a network of 100 passive NO₂monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking.</p> <p>Results</p> <p>There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO₂LUR OR = 1.86 (95%CI, 1.59–2.16) in all girls and OR = 2.98 (95%CI, 0.98–9.06) for older girls, over an interquartile range increase and controlling for confounders.</p> <p>Conclusion</p> <p>Our findings indicate that traffic-related pollutants, such as NO₂, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.</p

Carbon monoxide emissions from passenger vehicles: predictive mapping with an application to Hamilton, Canada

Integrated urban models are designed to simulate land-use and transportation interactions and to allow predicts of traffic volume and vehicle emissions at the link level of the urban transport network. As such, these models can address the weakness of existing systems. The Integrated Model of Urban LAnd-use and Transportation for Environmental analysis is an operational urban model calibrated for the Census Metropolitan Area of Hamilton. This paper extends this model to include air pollution estimation and mapping of vehicle air pollutants, employing a dispersion model and spatial data analysis. The approach provides an integrated framework for impact assessment of land-use and transport policies on traffic flows, emissions, and pollutant concentration, enabling the evaluation of population exposure to traffic related pollution. The study illustrates how vehicle-generated carbon monoxide concentration can be estimated and mapped using the proposed approach under a base-case scenario for the 2006. Several development and transportation scenaria can be developed and ‘hot-spots’ of traffic-originated air pollution can be identified and visualized within a Geographic Information System framework

Will alternative fuelled vehicles contribute to solving the sustainability problem? A framework for assessing future vehicle demand [Abstract]

It is widely accepted that the transportation system is a major contributor of greenhouse gas emissions, leading to an unsustainable future. While the problem is more intense in the developed countries, in which the transportation sector is responsible for approximately twenty five to forty percent of the overall greenhouse emissions, the developing world is following suit. Recent trends indicate an increasing demand for automobiles, in these countries, with potentially dire environmental consequences for the planet. Meeting the growing demand for personal mobility and transport of goods in a sustainable way represents a wide range of interrelated technological and public policy challenges. A number of strategies for fulfilling sustainability targets impose improvements in vehicle technology and fuels. Alternative fuelled vehicles offer hope for drastic reductions in air pollution and greenhouse gas emissions. One the most important challenges regarding alternative fuelled vehicles is devising strategies that will lead to the success of these vehicles in the market. In this respect, an important dimension is to understand household preferences regarding certain vehicle characteristics, fuel availability and policies affecting a vehicle’s end price. Since alternative fuelled vehicles are not widely available, researchers resort to innovative approaches for assessing their demand. One such method is known as stated choices. The method uses experimental design to create sets of hypothetical vehicle options using a number of vehicle attributes. This paper describes a framework for assessing the demand for alternative fuelled vehicles and its application to the Census Metropolitan Area of Hamilton, Canada. Data collection is conducted using contemporary internet-based surveying techniques and respondents are recruited via e-mail. The overall procedure involves two stages. First, respondents are asked to provide information about their household, residence and vehicles currently owned. A second stage in the survey employs the stated choices experiment where respondents choose the vehicle they would most likely buy from a hypothetical set of vehicles that use gasoline, a mix of fuels (hybrid) or an alternative fuel. Results from this study will contribute to scenario building with the purpose of examining the implications of policies, regarding the potential contribution of alternative fuelled vehicles to emission reduction objectives and the sustainability of the transportation system

Household demand and willingness to pay for clean vehicles

This paper examines the factors and incentives that are most likely to influence households’ choice for cleanervehicles in the metropolitan area of Hamilton, Canada. Data collection is based on experimental design and stated choice methods through an Internet survey. Choice alternatives included a conventional gasoline, a hybrid and an alternative fuelled vehicle. Each option is described by a varying set of vehicle attributes and economic incentives, customized per respondent. Controlling for individual, household and dwelling-location characteristics, parameters of a nested logit model indicates that reduced monetary costs, purchase tax relieves and low emissions rates would encourage households to adopt a cleanervehicle. On the other hand, incentives such as free parking and permission to drive on high occupancy vehicle lanes with one person in the car were not significant. Furthermore, limited fuel availability is a concern when households considered the adoption of an alternative fuelled vehicle. Finally, willingness-to-pay extra for a cleanervehicle is computed based on the estimated parameters

Modelling car ownership in urban areas: a case study of Hamilton, Canada

This paper examines the influence of family structure, socio-economic characteristics and accessibility at the place of residence on the number of cars owned by a household. Special attention is given to the neighbourhood characteristics, which are quantified by introducing several measures of neighbourhood proximity to out-of-home amenities and land-use derived from fine-grained spatial data with the help of GIS. For the purposes of our analyses, we used micro-level data obtained through a recent Internet-survey that was conducted in the Census Metropolitan Area of Hamilton, Canada. We find that household life-cycle stage, socio-economic factors, mixed density at the traffic analysis zone level and land-use diversity within walking distance from the place of residence influence households’ decision on how many vehicles to own. The results can be used to advise the design of planning policies aiming at controlling the effects of excessive carownership and mobility