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Exploring the self similar properties for monitoring of air quality information
Abstract
Air quality information has assumed much importance over the years due to the increase in air pollution. One major hindrance in monitoring of air pollutants is the dearth of spatial availability of aerosol concentration measurements due to the cost involved in deployment of sensors. In this respect, self similarity analysis of data can be very useful. This work is based on standard grid based pollutant dispersion models in a simulated environment over different scales of grid size. The fractal dimension is considered as a scale invariant metric which gives an idea about the variation in pollutant concentration across different scales. A method is detailed for measuring the fractal dimension properties. Results indicate that it is possible to apply the dispersion models across different scales and also the air quality monitored in one region can be compared with other regions.
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Remote sensing techniques are applied to sodar data for providing information regarding the planetary boundary layer. The temperature inhomogeneity that results from convective mixing in lower atmosphere is responsible for the backscattering of sound wave transmitted by the sodar. The intensity of echo returns fluctuates with time in the mixing height zone due to convection. In this work, fractal dimension (FD) of the data is considered as a measure for classifying the mixing height zone. A method has been proposed to tackle the noisy sodar data without losing relevant meteorological information. A comparison of the computed FD for noise-prone sodar data sets of tropical urban settings with those of relatively noise-free Antarctic settings is reported in this context.
Air pollution is growing at an alarming rate across India, with urban regions in particular caught in the toxic web. Although there are stringent acts and regulations for air quality, proper information on air quality is not available round the clock. This work looks at the requirements for the design of a genuine air quality information system. Peri-urban regions of Howrah, with its cluster of foundries and other age-old industries located within densely populated residential zone, is considered as a case study. Air quality data available for this zone is analyzed and compared with that of other Indian cities. The prospects of designing an information system to monitor air quality of this region is explored in the study.
The rapid increase of aerosols over the Indian Subcontinent over the last decade has the potential for severe health implications. However, the lack of a dense network to measure PM2.5 (particles with aerodynamic diameter < 2.5 μm) hinders health risk assessments at regional scale. Here, we utilize Multiangle Imaging SpectroRadiometer (MISR)-retrieved columnar aerosol optical depths to estimate surface PM2.5 based on recently published conversion factors that account for the composition and vertical distribution of aerosols. We examine the space–time variability of bias-corrected (utilizing coincident in-situ observations) PM2.5 over the Indian Subcontinent for the period Mar 2000–Feb 2010. We show that 51% of the subcontinent's 1.4 billion people are exposed to pollution that exceed the World Health Organization's highest annual air quality threshold of 35 μg m− 3, while another 13% and 18% are exposed in the ranges 25–35 and 15–25 μg m− 3 respectively. Of the remaining population who breathe clean air, only 25% live in urban areas. In many regions, the high-levels of pollution are persistent rather than episodic. PM2.5 concentrations in the rural areas of the Indo-Gangetic Basin are higher than many urban centers in peninsular India. Five hotspots (where PM2.5 increases by > 15 μg m− 3 over the ten-year period) are identified, which cover parts of the eleven Indian states and Bangladesh affecting ~ 23% of the population. Our results highlight the urgent need to carry out local cohort studies at these hotspots to better understand the health impacts under local conditions.
This paper provides the first review of the application of atmospheric models for particle dispersion. The different types of dispersion models available, from simple box type models to complex fluid dynamics models are outlined and the suitability of the different approaches to dispersion modelling within different environments, in regards to scale, complexity of the environment and concentration parameters is assessed. Finally, several major commercial and non-commercial particle dispersion packages are reviewed, detailing which processes are included and advantages and limitations of their use to modelling particle dispersion. The models reviewed included: Box models (AURORA, CPB and PBM), Gaussian models (CALINE4, HIWAY2, CAR-FMI, OSPM, CALPUFF, AEROPOL, AERMOD, UK-ADMS and SCREEN3), Lagrangian/Eulerian Models (GRAL, TAPM, ARIA Regional), CFD models (ARIA Local, MISKAM, MICRO-CALGRID) and models which include aerosol dynamics (GATOR, MONO32, UHMA, CIT, AERO, RPM, AEROFOR2, URM-1ATM, MADRID, CALGRID and UNI-AERO).
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