Health and Air Quality Regulation in Delhi, India
Health and Air Quality Regulation in Delhi, India
The primary general area of concern expressed by the referees on the previous version of this proposal was that it was unclear that our methods would allow us to to accurately assess past as well as present exposure to adverse air quality and to integrate these into an assessment of the impacts of the particular regulations introduced in Delhi. We address these issues by augmenting our survey to include more detailed retrospective information as well as through direct measurement of internal and travel air quality on a sampled basis. We also provide more focused methodological discussion that shows how we plan to exploit this information in conjunction with remotely sensed data on air quality to evaluate the effects of these regulations by taking advantage of both temporal and spatial variation.
Response to specific criticisms and Issues
Critique-1:
The first referee expressed concern about the absence of a clear temporal dimension in the context of our analysis. We agree that the model was, for notational simplicity and given space constraints, essentially static. We certainly intended to incorporate a strong temporal dimension and, in particular, to take advantage of the fact that there are long-term impacts of air quality on respiratory health. Thus a comparison across cohorts that were differentially influenced by changes in air quality due to different residence and commuting patterns provides an instructive basis for inference. This revision thus provides more detail as to how temporal variation will be exploited.
The first referee also wonders how it is that air quality might affect commuting patterns. Clearly a change in air quality will affect the desirability of living in a particular area. If this change in desirability results in movement then it will influence commuting patterns. Second, the process of zoning to improve air quality can affect commuting by moving jobs away from residences. We note that at this point we take the notion that people may move away from areas with poor air-quality as a possibility that arises naturally from a model of residential choice, not something that we necessarily expect to find. If we do not find such an effect we will attempt to address whether this is because people have low willingness to pay for good quality air or because they cannot adequately assess differences in air quality.
Concerns were also expressed about the extent to which the regulatory changes, given possibly lax enforcement and air movement, might have had an important effect on the spatial distribution of adverse air quality. Preliminary evidence suggests that these effects have indeed been large. As far as land-use enforcement is concerned, areas where polluting industries are shut down should experience cleaner air. Thus older industrial zones within the city should show improved air quality and newly developed industrial clusters in the peripheral areas of the city are likely to experience elevated levels of air pollution. The data indicate that 24,000 industries have moved to these clusters and thus the laws have clearly had an impact. We also have evidence to suggest that the consequences of CNG regulation have been to move a large number of gasoline operated vehicles to the neighboring districts of the city thus exacerbating differences in air quality between the inner city and the outlying areas. Preliminary analysis of the monitored data from the 150 stations suggests that indeed air quality is relatively high in the older industrial districts (Figure 6). Analysis of the remote sensed data will permit evaluation of the extent to which this represents a change occurring at the time the interventions were introduced, as we expect.
The first referee also indicated we could make use of Co-Kriging methods. As discussed in the revised proposal we will do so to interpolate spatial pattern of air pollution for the year 2005 for four different seasons. We also discuss in greater detail the key objective of monitoring air pollution at 150 site is to validate and establish relationship between ground measured TSP with satellite retrieved AOT.
Critique-2
A primary concern was raised by this referee regarding sources of exposure to adverse air quality other than through current exterior air quality at the place of residence and business. We agree that it is both important to measure exposure in other venues such as through interior air and cumulative exposure over time due to changes in residence and/or air quality within residence. We have thus developed a new strategy for indoor and traveling related exposure assessment. We recognize that indoor air quality can be a function of several factors including building type, ventilation, air conditioning, geographic location, cooking fuel used, number smokers in the household, time spent on cooking etc. On a sample basis we will thus monitor indoor air quality in 10% of the selected households and estimate a simple indoor-air quality production function that can then be used to predict indoor-air quality in all households.
With regard to travel, we will collect air sample along different routes using different modes of transportation for different times of the day in all four seasons. This approach will allow us to establish the degree of travel related exposure based on route, duration and mode of transport used. Questions on mode and time of transport, are covered in the individual part of the survey and will be exploited for estimating travel related exposure to air pollution.
We have also added questions on cooking fuel used in the household over life time and residential history in terms of distance from Delhi and place characteristics [in terms of urbanness] and mode of transportation used. These can be used to provide some, albeit imperfect, measures of lifetime exposure to adverse all quality for all respondents. We have also added discussion in the context of estimation to how cumulative exposure to adverse air quality and to the importance of measuring the relative importance of lagged and current exposure in order to measure the impacts of the court-ordered interventions. Obviously, the most detailed data on exposure, as is appropriate to the focus of this survey, will be available for the period immediately preceding and following the court-ordered interventions, taking advantage of the fact that the interventions were both temporally and spatially specific.
It was also suggested that we need to account for the multi-level nature of the analysis. We agree that given the explicitly spatial nature of our analysis it is important that we allow for unobservables that are correlated locally and that the proposal failed to mention this possibility. The household provides a natural way of grouping individuals in the family and we will certainly take advantage of this grouping to model error structures. The groupings for larger units of analysis are less clear because the boundaries of interaction, through, for example, the market for housing or through shared exposure to unobserved measures of adverse air quality. We therefore propose to make use of non-parametric and method of moments estimators of spatial autocorrelation to address this issue. These methods have been shown to be robust to small amounts of location specification, which seems a real issue given that the locations of the house and office are imprecise measures of the places at which an individual spends time when he is at home or at the office, respectively.
Critique-3:
The third referee raises concerns about the need for cloud-free images to evaluate air quality. In fact we need only 4/5 clould free imageries for retrieving seasonal air quality indices for each year (section D.2.B). Delhi’s geographic location and climatic conditions are such that we can find the required number of cloud free scenes for each season for the past 10 years.
With regard to the selection of air quality stations, these are selected randomly in a 1km2 gird overlaid onto the study area (Section D.2.B), and we expect that these areas experience some degree of homogeneity in terms of land use and land cover patterns.
We agree with the referee that a strength of the proposal is the development of a structural model of the underlying processes and that ultimately estimation of that model is likely to have high payoff. However, we believe that the details of such a model should be informed by the less structural, but still theoretically informed, analyses that will be the initial focus of our analysis. Nonetheless, we have added a small section describing in general how we propose to carry out structural estimation and the key counterfactuals that we hope to carry out.
Preliminary Studies Progress: We have made significant progress over the last 6 months. With the generous support from PSTC, Brown University we have been able to collect particulate matter (PM) in a range of 1 to 10 micron from July 23 to December 3, 2003. The PI and Allen Chu at NASA are in the process of establishing a relationship between ground measured PM values, especially PM2.5 and MODIS satellite retrieved AOT at a coarser resolution (250meters). In addition, the PI and Co-PI are coordinating a socio-economic and respiratory health survey of 1500 households in Delhi. The sample was selected using newly developed location based sampling technique (Section D.2.A). We are likely to complete this survey by mid March, at which point we will start linking ambient exposure to AOT surfaces.
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related searches
- air quality lesson plan
- quality management in health care
- air quality health issues
- air quality and health
- air quality and health effects
- quality improvement in health care examples
- best air quality in usa
- air quality by state
- bad air quality facts
- air quality formula
- air quality statistics by state
- quality improvement in health care