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Managing Future Air Quality in Megacities: A case study for Delhi(Atmospheric Environment)
(Markus Amann, Pallav Purohit, Anil D. Bhanarkar, Imrich Bertok, Jens Borken-Kleefeld, JanuszCofala, Chris Heyes, Gregor Kiesewetter, ZbigniewKlimonta, Jun Liu, Dipanjali Majumdar, Binh Nguyen, Peter Rafaj, Padma S. Rao, Robert Sander, Wolfgang Schopp, Anjali Srivastava, B. Harsh Vardhan ,,,,Year : 2017)
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Megacities in Asia rank high in air pollution at the global scale. In many cities, ambient concentrations of fine particulate matter (PM2.5) have been exceeding both the WHO interim targets as well as respective national air quality standards. This paper presents a systems analytical perspective on management options that could efficiently improve air quality at the urban scale, having Delhi as a case study. We employ the newly developed GAINS-City policy analysis framework, consisting of a bottom up emission calculation combined with atmospheric chemistry-transport calculation, to derive innovative insights into the current sources of pollution and their impacts on ambient PM2.5, both from emissions of primary PM as well as precursors of secondary inorganic and organic aerosols. We outline the likely future development of these sources, quantify the related ambient PM2.5 concentrations and health impacts, and explore potential policy interventions that could effectively reduce environmental pollution and resulting health impacts in the coming years. The analysis demonstrates that effective improvement of Delhi's air quality requires collaboration with neighboring States and must involve sources that are less relevant in industrialized countries. At the same time, many of the policy interventions will have multiple co-benefits on development targets in Delhi and its neighboring States. Outcomes of this study, as well as the modelling tools used herein, are applicable to other urban areas and fast growing metropolitan zones in the emerging Asian regions
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Study of local and Regional influence of PM2.5 Concentration during Odd-even Rule in Delhi using Causual Analysis(Aerosol & Air Quality Research)
(A. B. Chelani
,175,,1190-1203,Year : 2017)
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PM2.5 concentration observed during odd-even rule in Delhi is analysed for assessing its effectiveness in curbing the levels. The local and regional influence is analysed by using similarity and causality analysis. Causality analysis is usually carried out by using nonlinear dynamical technique which predicts one variable using another. In this study a simple approach is presented based on nearest neighbour method. It is observed that PM2.5 in Delhi has regional influence in addition to local sources. Although the effectiveness of odd-even rule is not observed in curbing the PM2.5 levels, it is suggested that extended implementation of the rule may provide more insight to the impact. Similarity analysis suggested that PM2.5 concentrations in Delhi have somewhat similar temporal behaviour with neighbouring locations in the southeast (SE) and west (W)-southwest (SW) sector. The control policies in Delhi need to be adopted keeping in mind the local and regional influences on PM2.5 levels in the area
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Particulate and Gases Pollution Control During Idling Condition of Vehicles at Traffic Intersections: A Case Study for Nagpur City(American Journal of Earth Sciences)
(Navneet Kumar, Rajendra Prasad Poluru, Padma S. Rao, Mayuri Shrirang, Ashish P. Patil,,,,Year : 2017)
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Traffic intersections are major contributors for built-up of emissions of air pollutants like PM10, PM2.5, SO2 and NOx from automobiles. The study has been carried out for Nagpur, third largest city in the Indian state of Maharashtra. Emissions of PM10, PM2.5, SO2 and NOx quantified for one month, and control studies from motor vehicles during idling condition at a traffic signal were carried out at three foremost traffic signals of the city. The selected traffic intersections are Law College Square, Medical Square, and Shankar Nagar square. In this study selected parameters Air Metrics measured PM10 PM2.5 SO2 and NOx and followed by titrimetric gaseous impinger system and control of the same assessed by bench scale air pollution control (BAPC) system. Reduction percentages from the BAPC system were obtained as 57.68 & 47.65 for PM10 and PM2.5 respectively whereas for SO2, 70.55 and for NOx, 54.53 obtained
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Air Quality Index - A Comparative Study for Assessing the Status of Air Quality(Engineering and Technology)
(Shivangi Nigam, B. Padma S. Rao, N. K. Mandal, N Kumar, and C. Chauhan,06,No. 02,,Year : 2016)
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Air quality Index is a tool for identify the present scenario of air quality. Six different methods of estimating Air quality Index (AQI) based on four pollutants synergistic effect viz., PM 10 , PM 2.5 , SO 2 and NO 2 were used to compare the prevailing ambient air quality in the study region. The average concentration of PM10, PM 2.5 , SO 2 and NO 2 are in 82.59, 61.61, 27.19 and 3.92 µg/m 3 in was observed in May June respectively. Similarly the levels in June-July 2014 were observed as 57.96, 43.27, 14.24 and 2.54 µg/m 3 respectively while the concentration in July-August 2014 were found as 39.37, 32.89, 10.44 and 2.92µg/m 3 respectively, in August-September 2014 were 30.08, 32.53, 12.18 and 2.90 µg/m 3 respectively and the levels in Sept-Oct 2014 were found as PM 10 , PM 2.5 , SO 2 and NO 2 are in 93.66, 94.04, 23.39 and 6.85 µg/m 3 respectively. Seasonal and daily AQI calculation revealed that air quality status in the study region under various classes ranging from good, moderate, satisfactory and unacceptable class for different AQI calculation
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Characterization of Polynuclear Aromatic Hydrocarbons in fugitive of PM10 emissions from an integrated iron and steel plant
(V. V. Khaparde, A. D. Bhanarkar, Deepanjan Majumdar, C. V. Chalapati Rao,562,,155-163,Year : 2016)
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Fugitive emissions of PM10 (particles <10μm in diameter) and associated polycyclic aromatic hydrocarbons (PAHs) were monitored in the vicinity of coking unit, sintering unit, blast furnace and steel manufacturing unit in an integrated iron and steel plant situated in India. Concentrations of PM10, PM10-bound total PAHs, benzo (a) pyrene, carcinogenic PAHs and combustion PAHs were found to be highest around the sintering unit. Concentrations of 3-ring and 4-ring PAHs were recorded to be highest in the coking unit whereas 5-and 6-ring PAHs were found to be highest in other units. The following indicatory PAHs were identified: indeno (1,2,3-cd) pyrene, dibenzo (a,h) anthracene, benzo (k) fluoranthene in blast furnace unit; indeno (1,2,3-cd) pyrene, dibenzo (a,h) anthracene, chrysene in sintering unit; Anthracene, fluoranthene, chrysene in coking unit and acenaphthene, fluoranthene, fluorene in steel making unit. Total-BaP-TEQ (Total BaP toxic equivalent quotient) and BaP-MEQ (Total BaP mutagenic equivalent quotient) concentration levels ranged from 2.4 to 231.7ng/m(3) and 1.9 to 175.8ng/m(3), respectively. BaP and DbA (dibenzo (a,h) anthracene) contribution to total-BaP-TEQ was found to be the highest
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Real time Ambient Air Quality Status During Diwali Festival in Central, India(Environment Science)
(Nigam, S; Rao, P.S.; Mandal, N. K.; Kumar, N; Chauhan, C; Maishlkar, V. A.; Mishra, P. N.,05,Issue 3/4,,Year : 2016)
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In India, festivals are celebrated with lot of enthusiasm and Diwali is the major festival of light. In this festival, houses are illuminated by lights and sky is illuminated by fireworks. These fireworks though create lot of amusement but also pollute the atmosphere in terms of air pollution. The continuous air pollution monitoring was undertaken during Diwali festival (2014) at residential site NEERI, Nagpur. Air quality parameters were compared with CPCB standard. On Diwali day, PM 10 and PM 2.5 concentration achieve its highest value of 900 µg/m 3 and 950 respectively µg/m 3. This high concentration is maintained in atmosphere for two days of this festival in atmosphere which is approximately 8-9 times more than that regulatory standard. These particles carry all the components of the cracker including heavy metals, alkali metals, alkaline earth and change the atmosphere with positive and negative ions apart from impaction of sulfur and other acid gases to the atmosphere
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Determining Heavy Metals Contamination in Road Dust in Delhi City, India(Atmosphere)
(B. S. Rajaram, P. V. Suryawanshi, C. V. Chalapati Rao
,29 ,Issue 3,221-234,Year : 2016)
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Road dust samples were collected from four different areas having different landuse patterns: industrial, heavy traffic, residential and mixed use in Delhi city of India. The samples were analyzed for Ba, Co, Cr, Cu Fe, Mn, Ni, Pb and Zn by ICP-AES. Results indicate high levels of Co, Cr, Cu, Mn and Ni in samples collected from industrial area. Ba, Pb and Zn showed higher concentration levels in heavy traffic area while Fe did not show any discernible variation between the localities. The concentrations of Fe, Mn, Ba, Zn, Cr, Cu, Pb, Ni and Co showed a decreasing trend. The content of heavy metals was comparable to those in other cities in the world. A multivariate statistical approach which includes Pearson's correlations and principal component analysis was used to identify the possible sources of metals in the road dust. Enrichment factors were estimated for further confirming the sources of contamination. Significant positively correlations between road dust metals Cu-Mn-Co-Cr-Ni suggest that major common source of origin is industrial activities. A meaningful correlation between Ba and Zn, and a moderate positive correlation between Pb and Ba indicate the influence of traffic activities. Enrichment factors calculation indicated that Pb, Cu, Cr and Zn are moderately enriched whereas Co, Ni and Mn are less enriched while Ba exhibited very low enrichment in the dust samples. The results indicate that industrial and vehicular traffic are the two major sources. Traffic appears to be responsible for the high levels of Zn, Cu and Ba. High concentration of Co, Cr, Cu and Mn may be due to industrial sources
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Disposition of Lightning Activity Due to Pollution Load during Dissimilar Seasons as observed from Satellite and Ground-Based Data(Climate)
(Anirban Middey and Pankaj B. Kaware
,4,,28,Year : 2016)
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The precise role of air pollution on the climate and local weather has been an issue for quite a long time. Among the diverse issues, the effects of air pollution on lightning are of recent interest. Exploration over several years (2004 to 2011) has been made over Gangetic West Bengal of India using lightning flash data from TRMM-LIS (Tropical Rainfall Measuring Mission-Lightning Imaging Sensor), atmospheric pollutants, and rainfall data during pre-monsoon (April and May) and monsoon (June, July, August and September) seasons. Near-surface pollutants such as PM10 and SO2 have a good positive association with aerosol optical depth (AOD) for both the pre-monsoon and monsoon months. High atmospheric aerosol loading correlates well with pre-monsoon and monsoon lightning flashes. However, rainfall has a dissimilar effect on lightning flashes. Flash count is positively associated with pre-monsoon rainfall (r = 0.64), but the reverse relation (r = −0.4) is observed for monsoon rainfall. Apart from meteorological factors, wet deposition of atmospheric pollutant may be considered a crucial factor for decreased lightning flash count in monsoon. The variation in the monthly average tropospheric column amount of NO2, from the Tropospheric Emission Monitoring Internet Service (TEMIS), is synchronic with average lightning flash rate. It has a good linear association with flash count for both pre-monsoon and monsoon seasons. The effect of lightning on tropospheric NO2 production is evident from the monthly average variation in NO2 on lightning and non-lightning days
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Evaluation of coarse and fine particles in diverse Indian environments(Environment. Sci.)
(K. V. George, Dinakar D. Patil, Mulukutla N. V. Anil, Neelkamal, Babu J. Alappat, Prashant Kumar,24(4) ,,3363-3374,Year : 2016)
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The estimates of airborne fine particle (PM2.5) concentrations are possible through rigorous empirical correlations based on the monitored PM10 data. However, such correlations change depending on the nature of sources in diverse ambient environments and, therefore, have to be environment specific. Studies presenting such correlations are limited but needed, especially for those areas, where PM2.5 is not routinely monitored. Moreover, there are a number of studies focusing on urban environments but very limited for coal mines and coastal areas. The aim of this study is to comprehensively analyze the concentrations of both PM10 and PM2.5 and develop empirical correlations between them. Data from 26 different sites spread over three distinct environments, which are a relatively clean coastal area, two coal mining areas, and a highly urbanized area in Delhi were used for the study. Distributions of PM in the 0.43–10-μm size range were measured using eight-stage cascade impactors. Regression analysis was used to estimate the percentage of PM2.5 in PM10 across distinct environments for source identification. Relatively low percentage of PM2.5 concentrations (21, 28, and 32%) in PM10 were found in clean coastal and two mining areas, respectively. Percentage of PM2.5 concentrations in PM10 in the highly urbanized area of Delhi was 51%, indicating a presence of a much higher percentage of fine particles due to vehicular combustion in Delhi. The findings of this work are important in estimating concentrations of much harmful fine particles from coarse particles across distinct environments. The results are also useful in source identification of particulates as differences in the percentage of PM2.5 concentrations in PM10 can be attributed to characteristics of sources in the diverse ambient environments
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Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)(Natural Hazards)
(C. S., Goswami S., Middey A., Das D., Chowdhary S.,75,,1369-1388,Year : 2015)
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Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department.
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An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity(Natural Hazards)
(Chaudhari S, Das D, Middey A.,76,,63-81,Year : 2015)
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The purpose of this study was to develop a computing system (CS) with fuzzy membership and graph connectivity approach to estimate the predictability of thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The stability indices are taken to form the inputs of the CS. Ten important stability indices are selected to prepare the input of the fuzzy set. The data analysis during the period from 1997 to 2006 led to identify the ranges of the stability indices through membership function for preparing the fuzzy inputs. The possibility of thunderstorms with the given ranges of the stability indices is validated with the bipartite graph connectivity method. The bipartite graphs are prepared with two sets of vertices, one set for three membership functions (strong, moderate and weak) with the stability indices and the other set includes the three membership functions for the probability of thunderstorms (high, medium and low). The percentages of degree of vertex (ΔG) are computed from a sample set of bipartite graph on thunderstorm days and are assigned as the measure of the likelihood of thunderstorms. The results obtained from graph connectivity analysis are found to be in conformity with the output of fuzzy interface system (FIS). The result reveals that the skill of graph connectivity is better and supports the FIS in estimating the predictability of thunderstorms over Kolkata during the pre-monsoon season. The result further reveals from the minimum degree of vertex connectivity that among the ten selected stability indices, only four indices: lifted index, bulk Richardson number, Boyden index and convective available potential energy, are most relevant for estimating the predictability of thunderstorms over Kolkata, India.
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An investigation on the evolution process of thunderstroms over a metropolis of India using DWR Max_Z products and genetic algorithm. (Meteorology and Atmospheric Physics)
(S. C., Khan F., Pal J, Goswami S., Middey A.,127,,75-93,Year : 2015)
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Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department.
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Effects of unregulated anthropogenic activities on mixing ratios of volatile organic air pollutants(Air & Waste Management Association)
(Rao P. S, Majumdar, Dipanjali ,65,Issue 9,,Year : 2015)
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During the months of October to November, many important festivals are celebrated in India. Celebration of these festivals are marked by extensive use of fireworks or pyrotechnics, bonfire, incense burning, open air community cooking, and temporary eateries using crude fuel such as coal, wood, kerosene, cow dung, burning of raw/semiwood, and coconut shells. The present study deals with the influence of these unregulated anthropogenic activities on ambient mixing level of volatile organic compounds (VOCs), especially some carbonyl compounds. The study was undertaken in the metropolitan city of Kolkata, India, with very high population density, which is even higher during festival period. The average total carbonyl level at different sites in Kolkata varied from 134.8 to 516.5 μg m(-3) in pre-festival season, whereas in post-festival season the same varied from 252.2 to 589.3 μg m(-3). Formaldehyde to acetaldehyde ratio altered from 0.62 in pre-festival season to 1.78 in post-festival season. Diurnal variation also altered, indicating variation in source composition of carbonyls. The total ozone forming potential calculated for all 14 carbonyls in pre-festival season increased by 35% in post-festival season. The effect of anthropogenic activities typical to the event of Diwali night characterized by intense execution of pyrotechnics resulted in significantly high level of carbonyl VOCs. Principal component analysis study for the event of Diwali shows clear contribution of the event on certain carbonyl VOCs. The results indicate elevated primary emissions of these pollutants and also their effect on formation of secondary pollutants. The study emphasizes the need of generating awareness among the communities in society as well as need for regulations to minimize the emissions and related hazards to the extent possible
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Exceedance analysis of PM10 concentration in central Indian city: predicting time between two exceedances(Aerosol & Air Quality Research)
(A. B. Chelani
,15(5),,2158-2167,Year : 2015)
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In this study the gap between the two exceedances is analyzed using time series analysis. The time series of PM_(10) (particulate matter of size less than 10 micron) observed during 2005-2013 in two cities; Nagpur and Chandrapur in central India is considered. Higher PM_(10) concentration is observed in Chandrapur as compared to Nagpur. Exponential relationship is observed between the average time between the two exceedances and annual average PM10 concentration. This information along with the PM_(10) concentration prediction model is utilized to predict the average number of observations between the two exceedances for the following year. ?k?-nearest neighbor approach is used for forecasting PM_(10) concentration which enabled estimating the average number of observations between two exceedances using exponential relationship. The approach can be used for estimating the average number of observations between the two exceedances over a year, which can further be utilized to make appropriate decision to control and manage high particulate matter pollution in an area.
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Nearest neighbour based forecast model for PM10 forecasting: Individual and combination forecasting, (Aerosol & Air Quality Research)
(A. B. Chelani
,15(3),,1130-1136,Year : 2015)
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Air quality forecasting using nearest neighbour technique provides an alternative to statistical and neural network models, which needs the information on predictor variables and understanding of underlying patterns in the data. k-nearest neighbour method of forecasting that does not assume any linear or nonlinear form of the data is used in this study to obtain the next step forecast of PM10 concentrations. Various function approximation techniques such as mean, median, linear combination and kernel regression of nearest neighbours are evaluated. It is observed that kernel regression of nearest neighbours outperforms the other individual models including bench mark persistence model for obtaining the next step forecasts. As the data may involve both linear and nonlinear patterns and any individual model cannot capture both types of patterns, combination forecasting is suggested as an alternative. The forecast error showed the outperformance of combination forecasting over individual forecast, which is quite obvious as it assigns more weightage to the model with minimum error. The study is useful when the data on predictor variables that influence the air pollutant concentrations is not available. The assumption on the underlying distribution of the data is also not required for the approach
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Study of temporal variations in aerosol optical depth over central India(I. J. Environmental Protection)
(A. B. Chelani
,5(1),,25-31,Year : 2015)
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Monthly aerosol optical depth (AOD) data over central India during 2001-2010 obtained from Moderate Resolution Imaging Spectroradiometer are analyzed for trend and periodicity. For this purpose, spectral analysis and linear trend analysis are performed. High AOD during monsoon followed by summer months are observed. Spatial analysis did not show any significant spatial variations in AOD levels. Spectral analysis suggested two dominant periods; 12 months and 6 months consistent with the annual and seasonal patterns. Trend analysis showed an insignificant trend at all the locations. Decadal change in AOD is the highest in Nagpur, which is an urban agglomeration station. Less developed and nonurban areas, however show decreasing or insignificant trend in AOD levels. Correlation with change in population over the last decade at different locations showed significant positive relationship with percentage change in AOD levels suggesting the effect of urban agglomeration on AOD in central India
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Prediction of remotely sensed cloud related parameters over an inland urban city of India(Annals of GIS)
(Navneet Kumar, Anirban Middey and Padma Rao,22,Issue 1,71-84,Year : 2015)
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Artificial neural network (ANN) is a mathematical model useful for forecasting on the any type of available data. This tool is not only useful in environment but also covers wide ranges of applicability. Utilizing this model, a study was carried out in an inland area of Nagpur for forecasting satellite-derived cloud parameters. Nine ANN architects are developed based on five pollutant parameter (aerosol optical depth, RSPM, SPM, SO2, NOx), meteorological and some cloud parameter. The models are used to simulate concentration of pollutants as well as the forecast and validation of cloud top temperature, cloud ice water path and cloud liquid water path during different seasons (winter, pre-monsoon and post-monsoon). Models based on back-propagation neural network were tested using the collected data of study area. The ANN models were trained using gradient descent algorithms to check the robustness and adaptability of the models. ANN models based on both satellite and ground-based data variables demonstrate the best performance and are skilled at resolving patterns of pollutant dispersion to the atmosphere during 2006–2013 for Nagpur city
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Long memory in air pollutant concentrations(Atmospheric Research)
(A.B. Chelani
,171,,1-4,Year : 2015)
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In the present paper, long-memory in air pollutant concentrations is reviewed and outcome of the past studies is analyzed to provide the possible mechanism behind temporal evolution of air pollutant concentrations. It is observed that almost all the studies show air pollutant concentrations over time possess persistence up to a certain limit. Self-organized criticality of air pollution, multiplicative process of pollutant concentrations, and uniformity in emission sources leading to self-organized criticality are few of the phenomena behind the persistent property of air pollutant concentrations. The self-organized criticality of air pollution is linked to atmosphere's self-cleansing mechanism. This demonstrates that inspite of increasing anthropogenic emissions, self-organized criticality of air pollution is sustained and has low influence of human interventions. In the future, this property may, however, be perturbed due to continuous air pollution emissions, which may influence the accuracy in predictions
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Investigation of Particulate Matter Performances in relation to Chalk Selection in Classroom Environment(Indoor & Built Environment)
(S. Goel, R. Patidar, K. Bari, RS Thakur
,26(1),,119-131,Year : 2015)
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This study aims to understand dust generation in classroom indoor for different chalk varieties and their potential doses to receptors in correlation with their physicochemical properties. Two representative chalks of extruded calcium carbonate and moulded gypsum type (total four) were used for writing on ceramic and wooden boards. Chalks were characterized using analytical techniques. Investigation of Particulate Matter (PM): PM2.5, PM10 and Total Suspended Particulate (TSP) concentrations in classroom air generated during the writing and wiping with chalk and board system was done. Dust collected beneath the board was analyzed for particle size distribution. Calcium carbonate made dustless chalks generate less PM during writing and wiping. They are quick settling and non-interacting with receptors, and deliver better utilization of material as compared to gypsum chalks. Physicochemical properties of the chalk constituents were invoked to explain the dust generation and its impact on the user. Surprisingly, dustless chalk made from Gypsum cannot be called really as dustless because of more PM emissions. The age-specific average potential dose for both PM10 and PM2.5 is higher for gypsum chalk as compared to the calcium carbonate chalks. The highest dose was estimated for the children in the age group of 6–11 years
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Future trend assessment of Regional Climate Variability screening past 20 years meteorological status(Int. J. of Advanced Scientific & Technical Research)
(Anirban Middey and Nimisha Jaiswal, 4 (5) ,,58-73,Year : 2015)
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The tasks of providing multi - decadal climate projections and seasonal climate predictions are of significant societal interest and pose major scientific challenges. The present study describes the global climate system context in which to interpret Nagpur and surrounding region environmental change to support planning and implementation of various strategies in the face of climate change. Here the classification and analysis of various climatic and meteorological parameters has been undertaken that have been proposed as relevant for understanding variations in climatic conditions of the Nagpur region (21.15 ?N, 79.09 ?E). The statistical and numerical analysis of past two decades data has been done. Two patterns of season stand out in our analysis i.e. the winter (December, January, February) and pre - monsoon (March, April, May). Some thermodynamic parameters (CAPE, CIN and sensible heat flux), rainfall, and surface evaporation along with planetary boundary layer have been studied in this work. The results obtained from the statistical analysis of past decades data are being utilized for predicting the future scenario using various trend projection techniques. These experiments, however, are only preliminary, and form the first stage of a wider study into how the climate variability occurs due to such meteorological parameters and in the future under various scenarios of future climate change
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