Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.     

Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis

This paper presents a statistical method for detecting distinct scales of pattern for mosaics of irregular patches, by means of perimeter–area relationships. Krummel et al. (1987) were the first to develop a method for detecting different scaling domains in a landscape of irregular patches, but this method requires investigator judgment and is not completely satisfying. Grossi et al. (2001) suggested a modification of Krummel's method in order to detect objectively the change points between different scaling domains. Their procedure is based on the selection of the best piecewise linear regression model using a set of statistical tests. Even though the change points were estimated, the null distributions used for testing purposes were those appropriate for known change points. The present paper investigates the effect that estimating the change points has on the underlying distribution theory. The procedure we suggest is based on the selection of the best piecewise linear regression model using a likelihood ratio (LR) test. Each segment of the piecewise linear model corresponds to a fractal domain. Breakpoints between different segments are unknown, so the piecewise linear models are non-linear. In this case, the frequency distribution of the LR statistic cannot be approximated by a chi-squared distribution. Instead, Monte Carlo simulation is used to obtain an empirical null distribution of the LR statistic. The suggested method is applied to three patch types (CORINE biotopes) located in the Val Baganza watershed of Italy.     

Monitoring of Daily Integrated Exposure of Outdoor Workers to Respirable Particulate Matter in an Urban Region of India

It is more and more recognised that an estimation of the exposure of the population to air pollutants is more relevant than the ambient air quality, since it gives a better indication of health risk. Outdoor workers in an urban region are generally of low income status and are exposed to higher levels of both indoor and outdoor air pollution. Hence respondents from this population subgroup have been selected for this study. Outdoor workers are divided into two categories, viz. traffic constables and casual outdoor workers like watchmen, roadside shopkeepers etc. Most of the respondents are from the lower income group. Each respondent is monitored for a continuous 48-hour period. The sampling frequency is once a week.The study region is situated in the north-west part of the Greater Mumbai Municipal Corporation. It can be classified as industrial cum residential area. The daily integrated exposure of the outdoor workers consists of two major micro-environments, viz. occupational and indoor residential.A personal air sampler was used along with a cyclone to measure levels of Respirable Particulate Matter (RPM). The cyclone has a 50% removal efficiency for particle diameter of 5 m. Paired samples of PM10 (ambient) and RPM (personal) were collected to establish the correlation between them. The average 24-hour integrated exposure to RPM was 322 g/m3 and exceeded the corresponding PM10 level observed at the nearest Ambient Air Quality Monitoring Station by a factor of 2.25. The 90% confidence interval for this exposure is 283–368 g/m3. This study clearly demonstrates that the daily integrated exposure and therefore the health risk of outdoor workers in an urban area is significantly more serious than that indicated by ambient air quality data.     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

Identification of hot spot and safe zones of ambient air quality in North Maharashtra region,India

The continuous infrastructural and economic development exerted pressure on the environmental conditions of the North Maharashtra region. The air quality of North Maharashtra region was analysed for the identification of hot spots and safe zones of ambient air quality. The result shows air pollution at some part of North Maharashtra region. The levels of particulate matter were above the prescribed limits especially at Bhusawal, Dhule and Jalgaon Urban centres. The traces of polyaromatic hydrocarbons were also observed in the urban centres of study area. The binary ratios of the persistent prevailing polycyclic aromatic hydrocarbon (PAH)'s show great correlation with the vehicular exhaust in the study region. The higher levels of pollutants in the study region were recorded in the post-monsoon season. The present study is first of its kind in the North Maharashtra region and an attempt has been made to analyse the anthropogenic sources and concentration of air pollutants in the area. The air pollutants reduce the defence system of human body and lead into severe health consequences like damage of lung tissues with escalation in the existing respiratory and cardiovascular diseases.     

Sensitivity of normal theory methods to model misspecification in the calculation of upper confidence limits on the risk function for continuous responses

Normal theory procedures for calculating upper confidence limits (UCL) on the risk function for continuous responses work well when the data come from a normal distribution. However, if the data come from an alternative distribution, the application of the normal theory procedures may lead serious over- or under-coverage depending upon the alternative distribution. In this paper we conduct simulation studies to investigate the sensitivity of three normal theory UCL procedures to departures from normality. Data from several gamma, reciprocal gamma, and lognormal distributions are considered. The normal theory procedures are applied to both the raw data and the log-transformed data.     

The psychological benefits of indoor plants: A critical review of the experimental literature

People have been bringing plants into residential and other indoor settings for centuries, but little is known about their psychological effects. In the present article, we critically review the experimental literature on the psychological benefits of indoor plants. We focus on benefits gained through passive interactions with indoor plants rather than on the effects of guided interactions with plants in horticultural therapy or the indirect effect of indoor plants as air purifiers or humidifiers. The reviewed experiments addressed a variety of outcomes, including emotional states, pain perception, creativity, task-performance, and indices of autonomic arousal. Some findings recur, such as enhanced pain management with plants present, but in general the results appear to be quite mixed. Sources of this heterogeneity include diversity in experimental manipulations, settings, samples, exposure durations, and measures. After addressing some overarching theoretical issues, we close with recommendations for further research with regard to experimental design, measurement, analysis, and reporting.     

Estimating prevalence using composites

We are interested in estimating the fraction of a population that possesses a certain trait, such as the presence of a chemical contaminant in a lake. A composite sample drawn from a population has the trait in question whenever one or more of the individual samples making up the composite has the trait. Let the true fraction of the population that is contaminated be p. Classical estimators of p, such as the MLE and the jackknife, have been shown to be biased. In this study, we introduce a new shrinking estimator which can be used when doing composite sampling. The properties of this estimator are investigated and compared with those of the MLE and the jackknife.     

Temporal scale-induced uncertainty in load duration curves for instream-dissolved oxygen

Load duration curves were developed using the Hydrological Simulation Program FORTRAN (HSPF) for dissolved oxygen (DO) for the Amite River in Louisiana, USA. The concept of ‘dissolved oxygen reserve’, defined as the total quantity of DO, is introduced. The effect of temporal resolution on duration curves of DO reserve was examined using duration curves developed based on daily, weekly, biweekly, and monthly average data. Duration curves for DO exhibited high variability in the load estimated using daily data as compared to those based on biweekly and monthly data. A seasonal analysis revealed the trend in the DO reserve. The daily DO reserve for the Amite River at Port Vincent was 44,049.31 kg when daily summer data were used and 74,255.15 kg for daily annual data. A surplus of 10,691 kg of DO reserve was shown in the monthly data during critical summer months. The coefficient of variation (CV), used to define the temporal scale-induced uncertainty, was found to be linearly and inversely correlated with the logarithm of the time scale. Regression equations were developed to extrapolate near real-time flow and water quality data, greatly simplifying flow and water quality monitoring and reducing the cost involved in flow and water quality monitoring.     

Efficient and recyclable palladium enriched magnetic nanocatalyst for reduction of toxic environmental pollutants

In this paper, highly stable, powerful, and recyclable magnetic nanoparticles tethered N-heterocyclic carbene-palladium(II) ((CH₃)₃[email protected]₃O₄) as magnetic nanocatalyst was successfully synthesized from a simplistic multistep synthesis under aerobic conditions through easily available low-cost chemicals. Newly synthesized (CH₃)₃[email protected]₃O₄ magnetic nanocatalyst was characterized from various analytical tools and catalytic potential of the (CH₃)₃[email protected]₃O₄ magnetic nanocatalyst was studied for the catalytic reduction of toxic 4-nitrophenol (4-NP), hexavalent chromium (Cr(VI)), Methylene Blue (MB) and Methyl Orange (MO) at room temperature in aqueous media. UV-Visible spectroscopy was employed to monitor the reduction reactions. New (CH₃)₃[email protected]₃O₄ magnetic nanocatalyst exhibited excellent catalytic activity for the reduction of toxic environmental pollutants. Moreover, (CH₃)₃[email protected]₃O₄ magnetic nanocatalyst could be easily and rapidly separated from the reaction mixture with the help of an external magnet and recycled minimum five times in reduction of 4-NP, MB, MO and four times in Cr(VI) without significant loss of catalytic potential and remains stable even after reuse.