Source Apportionment and Chemical Characterization of Trace Elements in Suspended Particulate Matter at Kolhapur City

Abstract

Ambient air pollution in an increasingly urbanized world directly threatens the health of a large fraction of the world’s population. There is a growing recognition that air-borne emissions from major urban and industrial areas influence both air quality and climate change on scales ranging from regional up to continental and global. Deteriorating urban air quality affects the viability of important natural and agricultural ecosystems in regions surrounding highly urbanized areas, and significantly influences regional atmospheric chemistry and global climate change. This challenge is particularly acute in the developing world where the rapid growth of megacities (cities having population equal to or more than 10 million) is producing atmospheric pollution of unprecedented severity and extent. For example, the deterioration of air quality is a problem that is directly experienced by a majority of the 300 million urban Indians, about 30% of India’s population. In developing countries, migration from the countryside to the megacities has brought as a consequence greater emissions into the atmosphere. This is mainly produced by the increase of vehicular traffic; a problem exacerbated by the tendency in these countries to have a stock of old and badly maintained vehicles and badly maintained roads. At the same time, the number of vehicles in circulation has increased as well. These factors have produced far-reaching changes in air quality in urban contexts, especially in the 1990s, when the majority of clean air plans were tightened up. The urban area has expanded with a result that urban population is now closer to Industrial establishments. Airborne particles which result from chemical and allied industries are nothing but heavy metals and aerosols contains reactive, corrosive and carcinogenic chemical molecules. Trace elements are released into the atmosphere by human activities, such as combustion of fossil fuels and wood, high temperature industrial activities and waste incineration. The combustion of fossil fuels constitutes the principal anthropogenic source of Ba, V, Co, Ni, Se, Mo, Sn, Sb, and Hg, and particularly of Cr, Mn, Cu, Zn, and As. High percentages of Ni, Cu, Zn, As, and Cd are emitted from industrial metallurgical processes. Exhaust emissions from gasoline may contain variable quantities of Ni, Cu, Zn, Cd, and Pb. Several trace elements are emitted through the abrasion of tires (Cu, Zn, Cd) and brake pads (Sb, Cu), corrosion (V, Fe, Ni, Cu, Zn, Cd) lubricating oils (V, Cu, Zn,Mo, Cd) or fuel additives (V, Zn, Cd, Pb). The platinum group elements, Rh, Pd and Pt, represent a relatively new category of traffic related trace elements in the environment, especially urban one, due to their application in automobile catalytic converters since the beginning of the 1980s. Studies of the transport and mobilization of trace elements up to now have attracted attention of many researchers. Trace elements are persistent and widely dispersed in the environment and interacting with different natural components result in toxic effects on the biosphere. Air pollution is a major health risk that may worsen with increasing industrial activity. At present, urban outdoor air pollution causes 1.3 million estimated deaths per year worldwide, according to the World Health Organization. The researchers studied the impact of human-made emissions on air quality, assuming past emission trends continue and no additional climate change and air pollution reduction measures (beyond what is in place since 2005) are implemented. Air pollution would also increase in Europe and North America, but to a much lesser extent than in Asia, due to the effect of mitigation policies that have been in place for over two decades. The results show that in 2025 and 2050, under the business-as-usual scenario studied, East Asia will be exposed to high levels of pollutants, such as nitrogen dioxide, sulphur dioxide and fine particulate matter (PM2.5). Northern India and the Arabian Gulf region, on the other hand, will suffer a marked increase in ozone levels. The analysis now published is the first to include all five major air pollutants know to negatively impact human health: PM2.5, nitrogen dioxide, sulphur dioxide, ozone, and carbon monoxide. The scientists considered pollutants released through human activity, as well as those occurring naturally such as desert dust, sea spray, or volcanic emissions. Taking all pollutants into account, eastern China, northern India, the Middle East, and North Africa are projected to have the world's poorest air quality in the future. In the latter locations this is due to a combination of natural desert dust and man-induced ozone. The effect of anthropogenic pollution emissions are predicted to be most harmful in East and South Asia, where air pollution is projected to triple compared to current levels. Increasingly, source apportionment analyses are being used as a relatively accurate, rapid, and cost-effective means of identifying and targeting sources and their relative contributions to the total pollution load. This scientific information helps air quality modellers as well as policy- and decision-makers. The data obtained from sourceapportionment studies provides policy- and decision-makers with practical tools to identify and quantify different sources of air pollution, increasing their ability to put in place effective policy and regulatory measures and control strategies to reduce air pollution to acceptable levels. Additionally, co-benefits can be realized – for example, source apportionment studies targeting specific air pollutants can also be used to assess climatic impacts, identify clean energy measures and greenhouse gas emission reduction strategies. Successful application of source apportionment (receptor modeling) methods and support to effective policy- and decision-making depends on the accuracy and relevance of the air quality measurements and the interpretations made by the scientist and air quality manager. Environment Canada’s air quality experts have included source apportionment studies and the improvement of source apportionment techniques and analyses as an integral component of their science and research, constantly seeking the highest quality of data and information. In India, Source Apportionment Studies have already been carried out in the cities of Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune. The primary focus of the study was on particulate matter, although it also deals with other pollutants like NOx, SO2, Ozone (O3), PM2.5, etc. Central Pollution Control Board, New Delhi has signed Memorandum of Contracts (MoCs) for source apportionment studies for the cities of Mumbai, Chennai and Kanpur with NEERI, IIT- Mumbai and IIT- Kanpur respectively and for source profiling for sources other than vehicles with IIT Mumbai. The Automotive Research Association of India (ARAI), Pune has conducted studies on emission factors for vehicles required as inputs in the studies. Based on the extensive literature survey on chemical characterization of trace elements and source apportionment studies abroad and in India, methodology adopted in Source factorizations, it was seen that PMF and CMB models were used to predict the quantification of trace elements in particulate matter from the different sources. In India, it was observed that the Source apportionments studies carried out by CPCB, New Delhi were carried out with the help of Chemical Mass Balance (CMB) model which need huge dataset of the sources.Both CMB and PMF provide quantitative estimates of the source contributions. In the CMB analysis, source profiles are provided whereas in PMF, the source profiles are estimated. If it is some of the source profiles are known, they can be used in PMF to constrain the extracted source profiles and thereby reduce the rotational indeterminacy. Both CMB and PMF are employing least squares fitting, but there are some important differences in how the underlying error structures are modelled and how many unknowns are being estimated. With PMF it is not possible to precisely assign errors to the source profiles and contributions. In a CMB analysis, it is possible to assign define error estimates to each source contribution value. However, there are no diagnostics provided in the CMB model that would alert the user to misspecification of the source profiles. Also since the CMB analysis is done on a sample-by-sample basis, there can be errors in the estimated source contributions because of the variations that can occur in the source profiles. PMF uses all of the data and thus, estimates the average source profile over the time interval during which samples were acquired. Thus, there are some similarities in the process and the outcome, but there are also some important differences in what is being estimated, the input data that is required, and the estimates of the uncertainties in the calculated values. Metropolitan cities selected for such studies had Source data available with CPCB. It is observed that Metropolitan Cities worldwide as well as India have adopted Source Apportionment Studies routinely and results are promising for better Air Quality Management in such cities. But II tier cities are, yet, to adopt such studies due to lack of source information and other necessary data to carry out Source Apportionment Studies. With the recent initiation of the Indian economic and the Investment policies, there is an increased growth in all the sectors in India along with an economic growth. The main areas of the property investments are the metro related areas and the tier I cities. For quite some time, most of the investors have been investing in these tier I cities and the metro related areas which has eventually saturated the areas. Both residential and the commercial investments have burgeoned these areas. Various large scale investments have made the realty sector quite fast along with an array of challenging projects. Apparently theunplanned development has resulted in the congestion of the city and is filled with residential and commercial properties. However, since all these main areas are congested, the government of these cities is forced to concentrate on alternative smaller cities like the Tier II cities. Few of the areas like Indore, Jaipur, Kochi, Ludhiana, Nasik, Nagpur and Chandigarh are in demand. The Indian real estate is now completely focusing in these areas and is extending new avenues for its development. The realtors are introducing an array of constructions, services and solutions for the development and the benefit of investors. Also the current situation and demand of the residential real estate market in the Tier I and the metro areas has forced the property investors to move on to the Tier II cities. As most of these cities are on the verge of development with wider infrastructure and open space unlike other metro related areas, most of the buyers prefer these areas. Apart from just the spread of residential projects in the Tier II cities, there is also an equivalent demand for the commercial property in these areas. As witnessed the retail real estate in India is on an upsurge and there is an array of retail markets and shops which are coming up in these areas on a fast pace. Also there is an increased change in the lifestyle of people and they have adopted the mall culture. Owning to all these aspects the developers find these cities more lucrative and also have various options for construction with better returns. Also the developers are witnessing greater investments in these areas when compared to metro cities. Most of the investors prefer investing in these areas because of the lesser time each project takes for construction and they also get better returns. The labour is cheap with safer markets along with a higher yield. Kolhapur in Maharashtra is fast growing II tier city in terms of Industrial Development, Co-operative movements, growing number of Agro based industries, leather industries and of course, tourism for which the city is all known. Hence, the present study was carried out in Kolhapur city to estimate and analyze chemically trace metals in SPM at Kolhapur City, which constitutes a long term threat to the health of general population. The results of source apportionment will be taken to appropriate dispersion model for accurate forecasting. The modelling and simulation will help urban planners and air quality planners for zoning policy decision making.The objectives of the present study were to to identify the locations for assessment of air quality based on wind direction/dispersion, to generate the required site specific data on topography/elevation, development activities etc. in selected locations for dispersion study, to measure Meteorological data i.e. Wind Speed, Wind Direction, Ambient Temperature etc. at selected locations for the study, to measure the concentration of Suspended Particulate Matter (SPM) at selected locations for the study, to characterize and quantify the presence of trace metals in SPM i.e. Cu, Pb, Ni, Zn, Cd, Mn, Fe, to identify the sources (Source Apportionment) of SPM by using United States Environmental Protection Agency (US EPA) Positive Matrix Factorization (PMF) Receptor model, to identify contribution of trace elements from various identified sources, to validate the results of modelling with characterization of trace elements in soil samples at selected locations by mass balance and to suggest methods and procedures for minimization of trace elements at selected locations The methodology as per standard protocol and laboratory practices was adopted to carry out experimental work as well as calculations of results to arrive at conclusions. Selection of three locations viz. Urban area like Shahu Blood Bank Corner on downwind direction of the city, Industrial area like Shiroli MIDC and Gokul Shirgaon MIDC, both on upwind direction of the city was done and collected wind data at these locations to build Wind rose to know the predominant wind direction and wind speed to be used for dispersion studies. High Volume Sampler and measured the concentration of SPM for 24 hours during July 2008 to July 2009 at all three locations mentioned above. Simultaneously, the soil samples were collected from each sampling locations. Acid digestion was carried out to extract trace elements from SPM collected from air as well as soil for determination and chemical characterization of trace elements i.e. Cu, Pb, Ni, Zn, Cd, Mn, and Fe using Atomic Absorption Spectrophotometer. The observed concentrations of trace elements were given as input to United States Environmental Protection Agency (US EPA) Positive Matrix Factorization (PMF) Receptor model predict the concentrations of trace elements on three locations. The US EPA PMF model was run under standard conditions mentioned and documented by US EPA and sources of each trace element were found out. Source Apportionment, which includes quantitative estimation of each source, was determined and percentage contribution of each source wascalculated. The validation was done on the basis of the results of PMF model with characterization of trace elements in soil samples at locations. The trace element/s with higher concentration at all locations was found out and detailed analysis was carried out to justify the cause of higher concentrations. The present study has been undertaken for identifying the sources of air pollution and their contributions to atmospheric pollutants in the form of particulate matter and the trace metal concentration in SPM in Kolhapur and its industrial at suburbs. The SPM samples were collected during July 2008 to July 2009 at three different sampling locations in the Kolhapur city (Maharashtra, India) namely Shahu Blood Bank Corner (SBBC) in the city, Gokul Shirgaon MIDC (GS) Area which is on South East of the city and Shiroli MIDC (SS) area which is on East of the city. Seven trace elements were analyzed using AAS. The first and most important pre-requisite in trace element analysis of atmospheric SPM is the precautions in processing and analysis of samples, to avoid contamination at each and every stage of sample collection and handling. The chemical composition data in particulate matter were analyzed using the US EPA PMF 3.0 receptor model to estimate the contributions from possible emission sources. Among all the criteria air pollutants, particulate matter (SPM and RSPM) have emerged as the most critical one in almost all urban areas of India. High SPM concentrations are primarily irritants but do not have much relevance for direct health consequences as compared to effects of its respirable fractions (PM10 and PM2.5). Due to this reason, the worldwide focus of monitoring is now increasingly being shifted to measurement of finer particles (PM10 and PM2.5), which can penetrate the human respiratory systems. Since 2010 the focus on suspended particulate monitoring has shifted to PM10 in India as well. Being a critical pollutant, PM10 has also been included in National Ambient Air Quality Standards. The present study was initiated in 2008 and monitoring was completed by 2010. The attempt was to characterize trace elements in SPM and quantification of contribution of each trace element to respective source. Source Apportionment study, first time is carried out for tier II city like Kolhapur, Maharashtra, India. US EPA PMF model is used since extensive source profiling based data not available. Model validation is done through measurement of emissions fromfoundries and traffic, which are major sources and also soil metal analysis. General suggestions for zoning of industries, traffic management and cause consequence study for health impact are suggested.