Spatial Modeling of Environmental Pollution and Ecological Risk

Spatial Modeling of Environmental Pollution and Ecological Risk provides valuable information and insights for researchers, students and professionals in geography, hydrology, sedimentology, soil science, agriculture, engineering and GIS as they face increasingly complex challenges around development strategies for a sustainable society. Written by the world’s leading researchers in their field, each article will begin with a short introductory essay that includes an overview of the sections' papers.

Individual chapters focus on the core themes of research and knowledge and some topics that have received lesser attention. Each chapter will review the current understanding of knowledge regarding the present study and scope and consider where future efforts should be directed.

• Discusses issues at the forefront of present research in environmental science, bioscience, ecology, pedogeomorphology, landscape, geoscience, forestry, hydrology and GIS
• Explores state-of-art techniques based on methodological and modeling in modern Deep learning and Machine learning geospatial techniques through case studies
• Describes novel control strategies, remediation and eco-restoration, and conservation techniques for sustainable development

• Language: English
• Publisher: Elsevier Science
• Release date: Dec 1, 2023
• ISBN: 9780323952835

Book preview

Preface

Spatial Modeling of Environmental Pollution and Ecological Risk is a comprehensive compilation that delves into critical dimensions of environmental pollution, emphasizing air pollution and environmental risk, aquatic environment, health, ecological risk, and soil environment. As our planet faces escalating environmental challenges, predominantly driven by anthropogenic factors, it is imperative to thoroughly comprehend the health of the total environment. This volume aims to unravel the intricate web of interrelated elements encompassing the lithosphere, hydrosphere, and atmosphere, collectively constituting our precious ecosystem.

In the recent decades, rapid and unplanned developmental projects and modern agricultural practices have severely impacted the health of the environment, especially in tropical and subtropical countries. The unbridled exploitation of natural resources and negligence toward environmental sustainability have exacerbated the health of our surroundings. The unforeseen silver lining of the COVID-19 lockdown was a brief respite, providing a glimpse of how nature could rejuvenate with reduced human intervention. The profound importance of balanced ecological health has never been more apparent.

This volume has been meticulously structured to cater to a diverse audience, including undergraduate and postgraduate students, scholars, scientists, and interdisciplinary readers. It offers a wealth of supporting materials, data, information, and methodologies, laying a concrete foundation for further research and development in this critical field. The chapters are thoughtfully organized, comprising introduction, methods and materials, results and discussions, and conclusion. Each section addresses the significance of the study, research gaps, limitations, novelty, major findings, future research prospects, alongside case studies, models, and illustrative examples.

The primary objective of this volume is to enrich the readers with new terms, advanced methodologies, and a thorough understanding of the subject matter. To facilitate enhanced comprehension, new terms and methodologies are highlighted in bold, and a comprehensive glossary is provided at the end of the book. The inclusion of accepted articles contributes to providing a holistic and contemporary perspective, equipping students, scholars, and scientists to navigate the complex landscape of environmental pollution and ecological risk.

As we navigate the challenges posed by an ever-changing environment, this volume stands as a beacon, offering insights, knowledge, and innovative solutions. It is our hope that this volume will spark curiosity, drive research, and foster collaboration to build a sustainable and harmonious relationship with our ecosystem.

Editors

Pravat Kumar Shit

Dilip Kumar Datta

Biswajit Bera

Aznarul Islam

Partha Pratim Adhikary

Part I

Air pollution and environmental risk

Outline

1. Introduction to air pollution and environmental risk

2. Air quality monitoring and its impact on local tree species in and around mining areas of Dhanbad District, Jharkhand, India

3. Environmental exposure to heavy metals in ambient air and its human health implications

4. Air pollution, disease burden, and health economic loss

5. Risk analysis of air pollution correlates with socioeconomic and heart diseases

6. Assessing the environmental pollution and risk of Metro rail construction in Dhaka City

7. Analysis of driving features for characterization of aerosol in India using shapely additive explanation (SHAP) and GIS

8. Magnetic susceptibility as proxy to air pollution: A case study from Durgapur industrial township, West Bengal, India

1: Introduction to air pollution and environmental risk

Biswajit Bera ¹ , Pravat Kumar Shit ² , Dilip Kumar Datta ³ , Aznarul Islam ⁴ , and Partha Pratim Adhikary ⁵       ¹ Department of Geography, Sidho Kanho Birsha University, Purulia, West Bengal, India      ² Department of Geography, Raja NL Khan Women's College (AUTONOMOUS), Midnapore, West Bengal, India      ³ Environmental Science Discipline, Khulna University, Khulna, Bangladesh      ⁴ Department of Geography, Aliah University, Kolkata, West Bengal, India      ⁵ PROGRAMME OF GROUNDWATER MANAGEMENT, ICAR-Indian Institute of Water Management, Bhubaneswar, Odisha, India

Abstract

This book, titled "Spatial Modeling of Environmental Pollution and Ecological Risk," explores various dimensions of air pollution, environmental risk, aquatic environments, health, and ecological risks associated with soil. Over the past few decades, the overall health of the environment has significantly deteriorated, largely due to anthropogenic causes. In tropical and subtropical countries, developmental projects and modern agricultural methods have been implemented without proper planning and management, resulting in poor conditions for the hydrosphere, lithosphere, and atmosphere throughout the year. However, the COVID-19 lockdown provided a temporary respite for the environment due to the prolonged shutdown of various economic sectors. The first section (Part-I) of this book focuses on air pollution and its associated risks. It provides an overview of different sources of air pollution, types of pollutants, causes, health effects, agricultural and economic impacts, ecological risk, reduction techniques, and remediation methods with case studies.

Keywords

Air pollution; Ecological risk; Environmental pollution; Health effects; Pollution control techniques; Spatial modeling

1.1. Introduction

Spatial Modeling of Environmental Pollution, and Ecological Risk book consists of two important parts i.e., part i: air pollution and environmental risk, part ii: aquatic environment, health, and ecological risk. Air pollution and environmental risk (Part-I) offers a flexible preamble to the study of manifold sources of air pollution, types of pollutants, principal causes, health effects, agricultural effects, economic effects, ecological risk, others effects, reduction and remediation techniques, air quality index and air quality health index and air pollution hotspots through spatiotemporal modeling, etc. (Brunekreef and Holgate, 2002; Jebril et al., 2022; WHO, 2022). Air pollution is the contamination of air or directly the existence of particles in the atmosphere that are detrimental to the wellbeing of humans, other living beings, and health of the total environment (Appannagari, 2017). It is also the contamination of indoor and outdoor either by chemical activities, physical or biological agents that modify the natural attributes or components of the atmosphere (Bera, Bhattacharjee, Sengupta, et al. 2021, Bera, Bhattacharjee, Shit, et al., 2021). There are lots of diverse types of air pollutants, such as gases (including ammonia, methane, carbon monoxide, nitrous oxides, sulfur dioxide, and chlorofluorocarbons), particulate matter (both organic and inorganic), and biological molecules (Bera et al., 2022). Air pollution can be the reason for various diseases, allergies, and even fatality to humans; it can also cause damage to other living organisms such as animals and plants, and it may harm the natural environment through acid rain, climate change, habitat and species extinction, and degradation. Globally, air quality is directly connected to the Earth's climate and ecosystem's health (Dimitriou and Christidou, 2011; Bera, Bhattacharjee, Sengupta, et al. 2021, Bera, Bhattacharjee, Shit, et al., 2021). Most of the providers of air pollution are also sources of greenhouse emission like burning of fossil fuel (Balat, 2007). Air pollution-related diseases include respiratory infections, heart disease, COPD, stroke, lung cancer etc. Poor air quality principally affects the cardiovascular system and body's respiratory system (Babatola, 2018).

Outdoor air pollution causes ∼3.61 million human deaths annually, while anthropogenic ozone along with PM2.5 contributes ∼2.1 million human death (Zhang et al., 2022). Overall, air pollution causes the deaths of around seven million people worldwide every year, or a global mean loss of life expectancy (LLE) of 2.9 years, and it is the world's biggest single environmental health risk (Allen et al., 2017). Indoor air pollution and poor urban air quality were listed as two of the world's worst toxic pollution problems in the 2008 Blacksmith Institute World's Worst Polluted Places report. Around 90% of the world's population inhales filthy air to relatively better air. Losses of productivity and degraded quality of life caused by air pollution have been projected to cost the world economy $5 trillion per year (Newbury et al., 2021). Various pollution control technologies and strategies have been discovered to reduce air pollution. Similarly, several international and national legislations and regulations have been framed to control the effects of air pollution (Rao et al., 2017).

1.2. Key aims of the part-I

This book entitled Spatial Modeling of Environmental Pollution, and Ecological Risk provides multiple dimensions regarding air pollution and environmental risk, aquatic environment, health, and ecological risk and soil environment, health and ecological risk. In the recent decades, health of the total environment is being deteriorated mostly due to anthropogenic causes. In tropical and subtropical countries, various developmental projects and modern agricultural methods are being executed without proper planning and management. As a result, health of the hydrosphere, lithosphere, and atmosphere shows worse conditions throughout the year. COVID-19 lockdown came as a blessing for the health of the total environment due to long-term shutdown of different sectors of economy. This part of this book will provide important supporting materials, data, and information for undergraduate and postgraduate students. This book will directly support scholars and scientists regarding data, metadata, methods and future research guidelines. This part of the book has been systematically organized to build concrete layout and morphology for the curiosity of interdisciplinary readers and scholars. This part of the book has been divided into various chapters which include introduction, methods and materials, results and discussions, and conclusion. These primary heads also contain significance of the study, research gap, limitations, novelty, major findings and future research and development whereas case study, models and examples have also been highlighted for more attention. New terms and methods would be demonstrated by bold text and a widespread glossary at the end of the book. Accepted articles have been considered in this book to get a new shape and impression for the students, scholars, and scientists in this contemporary challenging situation.

1.3. Organization of this part

The title of this book Spatial Modeling of Environmental Pollution, and Ecological Risk reflects the fundamental associations among environmental pollution, ecological risk and spatial modeling within lithosphere, hydrosphere, and atmosphere or broadly total environment. The contemporary researches in this field have been merged to know the diverse methods particularly for the health status of the total environment and combating the deterioration of environmental quality.

Chapter 2 covers the air quality monitoring and its impact on local tree species in and around mining areas of Dhanbad district, Jharkhand, India. This study examines the air quality index (AQI) and air pollution tolerance index (APTI) of the district's most prevalent perennial tree species. A multiple linear regression (MLR) model for AQI was applied using atmospheric metals as predictors. This scientific study showed that Ficus benghalensis is the most tolerant tree species to air pollution, while Alchornea cordifolia is the least tolerant. Therefore, authors suggested that F. benghalensis is the most effective tree species in this region for absorbing the air pollutants and environmental health improvement.

Chapter 3 presents the environmental and human health implications of metal exposure to ambient air pollution. This chapter scrutinizes the toxicity of heavy metals based on human health studies. This study also discusses various health impacts induced by heavy metals, including carcinogenic, pulmonary, reproductive, genetic, and neurologic health effects. It highlighted that the atmospheric air has a high capacity to absorb toxic heavy metals, allowing them to enter the human body by inhalation causing unfavorable physiological effects. This review article showed that the last century's industrial activities have resulted in huge increases in human exposure to heavy metals through inhaled air. Authors also enumerated that the most common heavy metals which caused human toxicity were arsenic, cadmium, chromium, lead, manganese, zinc, and nickel.

Chapter 4 highlights on air pollution, disease burden, and health economic loss. This study broadly focuses on the sources of air pollution such as factories, power plants, real-state constructions, automobiles, agriculture, and inappropriate waste disposal. It also shows that air pollution is a major threat to global health and prosperity. It causes millions of deaths annually on a global scale. Results show that high levels of air pollutants in the atmosphere are often connected with poor visibility, which affects the transportation industry, soil fertility, crop productivity, and subsequent revenue losses. This study also recommends some management techniques like planting trees, reducing unnecessary use of fuel-based vehicles, and avoiding burning leaves, trash, and other materials.

Chapter 5 emphasizes that the risk analysis of air pollution correlates with socioeconomic and heart diseases. The objective of this article is to provide an inclusive report on air pollution and cardiovascular diseases addressed to public health and regulatory policies. Result showed that high to critical levels of atmospheric air pollution and particulate matters (PM2.5, PM10), NOX, SO2, O3, CO, Pb, and smoke are responsible for health hazards. Consequently, they tried to establish both short-term and long-term elevations of pollutants which increase cardiovascular morbidity and mortality, and it accelerates acute vascular dysfunction, thrombosis, cardiac dysrhythmias, plaque instability, and deep-rooted atherosclerosis. This research also illustrated that socioeconomic parameters are among the most important drivers of population health. Statistical analysis stated that in the developed countries, airborne diseases are negatively correlated with the per head capita of the population, which means low income amplified heart disease risk.

Chapter 6 covers assessment of the environmental pollution and risk of Metro rail construction in Dhaka city. This study evaluates the environmental pollution and risk created from the Metro rail construction. This research highlighted that air and noise pollution are considered to regulate the environmental impacts of six major construction sites. They considered the parameters like SPM, PM2.5, PM10, SOX, NOX, and CO for air quality measurement. The results showed that the overall air quality of three sites such as Motijheel, Farmgate, and Kawran Bazar are unsafe. In terms of noise pollution, the average noise level is 78.4 dB which is much more than the tolerance limits in almost all stations. Noise pollution results indicated that noise pollution is highest in sensitive areas particularly in educational institutions like TSC (Dhaka University), hospital areas, and Farmgate area. Strict management policies should be applied to mitigate environmental impact generated by the Metro rail project in Dhaka mega city.

Chapter 7 highlights on analysis of driving features for characterization of aerosol in India using shapely additive explanation (SHAP) and GIS. Authors tried to evaluate the factors effecting the spatial distribution of PM2.5 over the past 4 years. They considered month of March for the period of 2019, 2020, 2021, and 2022 taking the picture of pre, during, and post-COVID-19 scenarios. The datasets used in the study include NDVI, LST, DEM, precipitation, and population density. The principal objective of the present study focuses on the driving forces acting on PM2.5 using an advanced machine learning approach based on SHAP and GIS analysis in India. Results showed that the features which are affecting the aerosol concentration from 2019 to 2022 in the month of March (premonsoon). R2 values showed that the model performed well in terms of predicting PM2.5 concentrations using the chosen independent features. The findings specify that meteorological characteristics had the biggest impact, followed by socioeconomic features and topographic features.

Chapter 8 represents magnetic susceptibility as proxy to air pollution: a case study from Durgapur Industrial Township, West Bengal, India. Authors showed that magnetic susceptibility provides suitable proxies for detecting pollution. It is due to sensible magnetite store house of toxic elements. Results showed that higher susceptibility values in areas with adverse vehicular traffic, other polluting sources, and revealing anthropogenic activities are the major reasons for the environmental degradation.

References

1. Allen J.L, Klocke C, Morris-Schaffer K, Conrad K, Sobolewski M, Cory-Slechta D.A.Cognitive effects of air pollution exposures and potential mechanistic underpinnings. Current Environmental Health Reports. 2017;4:180–191. doi: 10.1007/s40572-017-0134-3.

2. Appannagari R.R. Environmental pollution causes and consequences: a study. North Asian International Research Journal of Social Science and Humanities. 2017;3(8):151–161.

3. Babatola S.S. Global burden of diseases attributable to air pollution. Journal of Public Health in Africa. 2018;9(3). https://doi:10.4081/jphia.2018.813.

4. Balat M. Influence of coal as an energy source on environmental pollution. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2007;29(7):581–589. doi: 10.1080/15567030701225260.

5. Bera B, Bhattacharjee S, Sengupta N, Saha S. PM2.5 concentration prediction during COVID-19 lockdown over Kolkata metropolitan city, India using MLR and ANN models. Environmental Challenges. 2021;4:100155. doi: 10.1016/j.envc.2021.100155.

6. Bera B, Bhattacharjee S, Sengupta N, Saha S. Variation and dispersal of PM10 and PM2. 5 during COVID-19 lockdown over Kolkata metropolitan city, India investigated through HYSPLIT model. Geoscience Frontiers. 2022;13(1):101291. doi: 10.1016/j.gsf.2021.101291.

7. Bera B, Bhattacharjee S, Shit P.K, Sengupta N, Saha S. Significant impacts of COVID-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environmental health. Environment, Development and Sustainability. 2021;23(5):6913–6940. doi: 10.1007/s10668-020-00898-5.

8. Brunekreef B, Holgate S.T. Air pollution and health. The Lancet. 2002;360(9341):1233–1242. doi: 10.1016/S0140-6736(02)11274-8.

9. Dimitriou A, Christidou V. Causes and consequences of air pollution and environmental injustice as critical issues for science and environmental education. The Impact of Air Pollution on Health, Economy, Environment and Agricultural Sources. 2011:215–238.

10. Jebril N, Obaid K.M, Ali M.R, Muhsin Z.A, Halim Z.A. Air pollution during the strategy of COVID-19 lockdown in Iraq. In: 4th International Conference on Biological & Health Sciences (CIC-BIOHS’2022). 2022 doi: 10.2139/ssrn.4218046.

11. Newbury J.B, Stewart R, Fisher H.L, Beevers S, Dajnak D, Broadbent M, Pritchard M, Shiode N, Heslin M, Hammoud R, Hotopf M.Association between air pollution exposure and mental health service use among individuals with first presentations of psychotic and mood disorders: retrospective cohort study. The British Journal of Psychiatry. 2021;219(6):678–685. doi: 10.1007/s40572-017-0134-3.

12. Rao S, Klimont Z, Smith S.J, Van Dingenen R, Dentener F, Bouwman L, Riahi K, Amann M, Bodirsky B.L, van Vuuren D.P, Reis L.A. Future air pollution in the shared socio-economic pathways. Global Environmental Change. 2017;42:346–358. doi: 10.1016/j.gloenvcha.2016.05.012.

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2: Air quality monitoring and its impact on local tree species in and around mining areas of Dhanbad District, Jharkhand, India

Akash Mishra ¹ , Bindhu Lal ¹ , and Raj Kumar ²       ¹ Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Jharkhand, India      ² Forest Geo-informatics, Institute of Forest Productivity, Ranchi, Jharkhand, India

Abstract

The Jharia coalfield of Dhanbad, India, is the country's most important known source of prime coking coal. Coal extraction and utilization emit significant quantities of air pollutants, which are detrimental to the regional vegetation. This study aims to evaluate the air quality index (AQI) and air pollution tolerance index (APTI) of the district's most prevalent perennial tree species. AQI values were highest in the Jharia region and lowest in the Baramuri region, with PM10 and PM2.5 contributing the most to AQI values. Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were detected in the study area after analyzing the particulates for the presence of heavy metals. A multiple linear regression (MLR) model for AQI has been created using atmospheric metals as predictors. The effects of air pollution on the physiological and biochemical properties of plants can be analyzed by calculating the APTI of the tree species. Ficus benghalensis is the most tolerant tree species to air pollution, while A. cordifolia is the least tolerant. Therefore, F. benghalensis is the most effective tree species in the region for absorbing and regulating air pollution. With atmospheric metals as predictors, multiple linear regression models for the APTI of each tree species were developed to determine the effect of atmospheric metals on the plant's ability to tolerate air pollution.

Keywords

Air pollution index; Air pollution tolerance index; Coal mine; Heavy metals; Multiple linear regression

2.1. Introduction

India ranks fifth in the world in terms of coal reserves (148,787 million tonnes), and with an annual production of 778.19 million tonnes (MT) in 2021–22; it is the world's second-largest coal producer (Ministry of Coal, 2022). However, India's prime coking coal reserves are estimated to be only 4649 MT (Ministry of Coal, 2022), and Dhanbad in Jharkhand state is the country's largest producer of coking coal (Ghose and Majee, 2000). Coking coal is a critical component in the production of iron and steel. The primary use of coal in the steel industry is as a blast furnace fuel and for producing metallurgical coke for iron ore reduction or injection with a hot blast (Miller, 2013). Dhanbad has approximately 112 coal mines, most of which are open-cast coal mines (Dhanbad Municipal Corporation, 2017).

The environmental impact of open-cast coal varies depending on whether the mine is operational or abandoned and the geological conditions (Bell et al., 2001). All major mining activities, such as drilling; blasting; overburden excavation; dumping; removing natural vegetation; mining tailings; coal combustion processes; including mine fires, transportation, cleaning, and washing of coal fuel; and mine subduction, harm the environment, contributing a large volume of toxic air pollutants to the atmosphere, such as Particulate Matters (PMs), oxides of sulfur (SOX), oxides of nitrogen (NOX), fine coal dust, and metals (Pandey et al., 2014 a,b; Sandeep, 2018; Sonwani and Maurya, 2019). The air quality index is used to determine the extent of air pollution in a region (Mohan and Kandaya, 2007; Jiang et al., 2016). It projects the effects of polluted air on human health (Paulos et al., 2009). As shown in Table 2.1, the AQI is divided into six categories to represent the effects of air quality on health (Saddek et al., 2014). The AQI values up to 50 are considered good, and the AQI values of 51–100 represent moderate air quality conditions. The AQI values of 101–150 are unhealthy for sensitive groups, whereas if the AQI increases to 200, the air quality is unhealthy for everyone. Any further rise in AQI represents a very unhealthy to hazardous air quality.

Air pollution also harms vegetation (Emberson et al., 2001; Pandey et al., 2014b), and rising levels of air pollution endanger the stability of plant communities (Gupta et al., 2013; Ulrich et al., 2014). Plant physiological properties like photosynthesis, transpiration, and stomatal conductance are disrupted, as well as plant biochemical properties like chlorophyll and carotenoid content (Mudd, 2012). Gaseous pollutants, such as SO2 and NOX, enter leaves via stomata and dissolve in cells, converting to toxic forms such as sulfate, bisulfate, nitrite, and nitrate ions (Gheorghe and Ion, 2011), whereas particulates accumulate on the leaf surface, interfering with plant physiological activities such as photosynthesis, transpiration, and stomatal activity (Pandey et al., 2014b). Some atmospheric particulates enter plants via the foliar transfer pathway, which includes stomata, cuticular cracks, lenticels, ectodesmata, and aqueous pores (Shahid et al., 2017). These toxins stress plants by causing oxidative and water stress.

Table 2.1

Source: cpcb.nic.in.

Plants release antioxidants such as ascorbic acid or regulate their physiological functions by increasing the intercellular CO2 concentration to overcome or tolerate stress conditions. The air pollution tolerance index (APTI) assesses different plants' ability to tolerate air pollution. If the plant species' APTI is higher, it can tolerate air pollution more remarkably. Plants with APTI 16 are considered sensitive and serve as a bioindicator for air pollution, whereas plants with APTI >16 are considered tolerant and serve as a sink for air pollution (Rai, 2016). The effect of atmospheric metal on APTI, on the other hand, has never been quantified. The current study aims to determine the air quality index and air pollution tolerance index of various plant species in Dhanbad. The interrelationship between various air pollutants, atmospheric metals, and plant physiological and biochemical properties was determined using multivariate statistics. Using multiple linear regression (MLR) analysis, the effect of atmospheric metal on AQI and APTI of different plants was quantified. Furthermore, MLR models aided in developing mathematical equations for AQI and APTI predictions.

2.2. Materials and method

2.2.1. Study area

Dhanbad district is located in the Indian state of Jharkhand, between the latitudes of 23°39'–23°49' N and the longitudes of 86°11'–86°28' E, with an average elevation of 220 m. Air and plant samples were collected from four different sites in the district: Digwadih (the residential site), Chasnala and Jharia (the mining sites), and Baramuri Pahadi (the control site) (Fig. 2.1).

2.2.2. Air

2.2.2.1. Particulate matter

An air sampler was used to sample PM10, PM2.5, SO2, and NOX air pollutants. Particulate pollutants, PM10 and PM2.5, were analyzed using the gravimetric method, whereas gaseous pollutants, NOX, and SO2, were analyzed using the wet chemical method. For PM10 and PM2.5 analysis, the air is drawn through a size-selective inlet and passed through a 20.3 × 25.4 cm filter paper at a flow rate of 1132 L/min and a 47 mm polytetrafluoroethylene (PTFE) filter at a flow rate of 16.7 L/min, respectively. Particles with an aerodynamic diameter less than the cut-point of the inlet are collected by filter paper, and the air is directed through SO2 and NOX absorbing solutions. The mass of the particulates is determined by the difference in filter weights before and after sampling determined by the difference in filter weights before and after sampling. The concentration of PM in the designated size range is calculated by dividing the weight gain of the filter by the volume of air sampled.

Equation

where, Wf = final weight of filter paper after sample collection (g), Wi = initial weight of the conditioned filter paper before sample collection (g), V = total volume of air sampled (m³).

Figure 2.1  Study area map of Dhanbad district in Jharkhand state, India.

Equation

where, Qavg = average flow rate over the entire duration of the sampling (L/min), t = duration of sampling (min).

2.2.2.2. Metal concentration

The metal content of suspended particulate matter (SPM) was determined using an Optical 2100DV ICP-OES from Perkin Elmer, USA. On a hot plate, the PM10 and PM2.5 filter papers were digested. Filter papers were cut into small (1 cm) pieces and placed in a 250 mL beaker with 20 mL of extraction solution (3% HNO3 and 8% HCl) and heated for 30 min on a hot plate below 80°C. After that, the beakers are removed from the hot plate and cooled. After adding approximately 10 mL of reagent water to the remaining filter material in the beaker and allowing it to stand for at least 30 min, the extraction fluid in the beaker is transferred to a 100 mL volumetric flask diluted to make up the volume of 100 mL. The extraction solution is then filtered and sent to ICP-OES for analysis.

Equation

where, F a = total area of exposed filter (cm²), V = volume of air sampled (m³), Ft = area of filter taken for digestion (cm²).

2.2.2.3. Gaseous pollutants

The improved West and Gaeke method were used to analyze sulfur dioxide (SO2). SO2 was scrubbed in a known amount of tetrachloromercurate solution. One milliliter sulfamic acid (0.6% v/v), 2 mL HCHO (2% v/v), and 2 mL PRA (P-rosaniline hydrochloride) were added to the 10 mL absorbing solution. A UV-Vis spectrophotometer was used to measure the solution's optical density (OD) at 560 nm (Model UV-1800, Shimadzu corporation, Japan). After absorbing SO2 in tetrachloromercurate, it forms the stable and nonvolatile dichlorosulfite mercurate (HgCl2SO3 ²− ), which behaves effectively in solution as fixed SO3. Following the reaction of sulfite with formaldehyde and pararosaniline in an acid solution, reddish-purple pararosaniline sulfonic acid is produced in proportion to the concentration of absorbed SO2. A known amount of sodium metabisulfite was used to create a standard curve. The SO2 concentration was calculated as follows:

Equation

where, A S = Absorbance of sample, A b = Absorbance of reagent blank, CF = Calibration factor, V S  = Volume of sample (mL), V a = Volume of air sampled (m³), V t = Volume of aliquot taken for analysis (mL).

The reading of SO2 concentration in the sample is taken using a spectrophotometer. The standard curve for the spectrophotometer was drawn according to the procedure mentioned by CPCB (2009).

Nitrogen oxides (NOX) were analyzed using Modified Jacob and Hochheiser method. NOX was scrubbed in 10 mL of 0.1N sodium hydroxide solution for a known period using a portable gas sampler (Precision Instruments Ltd., India). To 10 mL of absorbing solution, 1 mL of H2O2 solution (30% v/v), 10 mL of sulfanilamide (20 g of sulfanilamide in 700 mL of distilled water and added 50 mL of 85% concentrated phosphoric acid and diluted to 1000 mL) solution and 1.4 mL of 1N NEDA were added with thorough mixing. OD was taken at 540 nm on UV-Vis spectrophotometer (Model 119, Systronics, India). The standard curve was prepared using a known amount of sodium nitrite solution. The concentration of NO2 is calculated as follows:

Equation

where A S = Absorbance of the sample, A b = Absorbance of reagent blank, CF = Calibration factor, V S  = Volume of the sample (mL), V a = Volume of air sampled (m³), V t = Volume of aliquot taken for analysis (mL),0.85 = Sampling efficiency.

The reading of NOX concentration in the sample is taken using a spectrophotometer. The standard curve for the spectrophotometer was drawn according to the procedure mentioned by CPCB (2009).

2.2.2.4. Air quality index

The Air Quality Index (AQI) is a simple and generalized way to describe air quality. The average sum of the ratios of three or more significant pollutant concentrations to their respective air quality standards was obtained. The air pollution index (API) was calculated by the formula given by Rao and Rao (1989).

Equation

where, SPM10, SPM2.5, SSO2, and SNO2 represent the ambient air quality standards for PM10, PM2.5, SO2, and NO2 respectively.

2.2.3. Plant

Based on the study of Pandey et al. (2014b), five tree species, namely, Butea monosperma, Ficus religiosa, Ficus benghalensis, Adina cordifolia, and Psidium guajava, were identified as the abundant tree species in the study region that were among the most tolerant tree species to the atmospheric pollution. The plant physiological and biochemical properties of these tree species were analyzed to determine the effect of atmospheric pollution on them.

2.2.3.1. Physiological properties

Photosynthetic rate (Ps), stomatal conductance (gs), and transpiration rate (E) were measured with the help of LICOR Portable Photosynthetic system (Model 6400, LICOR, Lincolin, NE, USA) at ambient climatic conditions on intact plants in the field. The system was calibrated using a known CO2 source (509 ppm concentration). The measurements were made on cloud-free days between 9.00 and 10.00 h. During the measurements, PAR ranged between 1100 and 1200 μmol/m²s.

2.2.3.2. Biochemical properties

Total chlorophyll and carotenoids were analyzed using a UV-Vis spectrophotometer. Photosynthetic pigments were extracted from leaf samples by homogenizing 100 mg fresh leaf in 10 mL of 80% acetone using a mortar and pestle. After filtering with a muslin cloth, it was centrifuged at 6000 × g for 15 min. The supernatant was collected, and the optical densities (D) were measured at 480 and 510 nm for carotenoids and 645 and 663 for chlorophyll a and chlorophyll b on a UV-VIS spectrophotometer (Model 119, Systronics, India). The amounts of total chlorophyll and carotenoids (mg/g fresh leaf) were calculated by using the following formulae given by Maclachlan and Zalik (1963) and Duxbury and Yentsch (1956), respectively:

EquationEquation

Total chlorophyll was calculated by adding the amount of chl a and chl b.

Equation

where V = Volume of the extract (mL), d = Length of the light path (cm), and W = Fresh weight of leaf (mg).

Leaf extract pH was determined by weighing 2 g of fresh leaves homogenized in 20 mL of double-deionized water. The supernatant was filtered through muslin cloth, and the pH of the leaf extract was determined by a pH meter (Cyberscan 510, Merck) after calibrating with buffer solutions of pH 4, 7, and 9.

For relative water content (estimated 95% moisture content on oven dry weight basis) estimation, 0.5 g of green leaf discs (6.0 mm) were floated in distilled water for 8 h. Then the discs were dried up using blotting paper and weighed again (saturation weight). The discs were then kept in the oven at 80°C for 24 h, and the oven dry weight was recorded. Relative water content was calculated by the following formula (Singh, 1977):

Equation

FW = Fresh weight, DW = Dry weight, and TW = Turgid weight.

Ascorbic acid content was estimated using the 2, 6 dichlorophenol indophenol (DCPIP) dye reduction method of Keller and Schwager (1977). For ascorbic acid determination, 500 mg of fresh leaf sample was homogenized with 20 mL of ice-cold extracting solution (500 mg oxalic acid and 0.75 mg EDTA dissolved in 100 mL distilled water). The homogenate was centrifuged at 6000 × g for 15 min. To 1 mL of supernatant, 5 mL of 2, 6 dichlorophenol indophenol (DCPIP) (20 μg/mL) solution was added with constant shaking, and the optical density of the pink color solution was taken at 520 nm wavelength (Es) on a UV-VIS spectrophotometer (Model 119, Systronics, India). The pink color was bleached by adding one drop of 1% ascorbic acid, and the OD of the bleached solution was taken at the same wavelength (ET). For blank (Eo), to 1 mL extracting solution, 5 mL of DCPIP solution was added, and optical density was taken at the same wavelength. A calibration curve was prepared by using varying concentrations of ascorbic acid. The ascorbic acid content was calculated as follows:

Equation

where C = Concentration of ascorbic acid read from the standard curve, w = Weight of leaf sample (mg), V = Volume of extract (mL), and v = Volume of supernatant taken for analysis (mL).

2.2.3.3. Air pollution tolerance index (APTI)

To assess the tolerance of plants against air pollution, plant species' air pollution tolerance index was determined by following the method of Singh and Rao (1983).

Equation

where A = Ascorbic acid (mg/g fresh weight), T = Total chlorophyll (mg/g of fresh weight), P = pH of the leaf extract, and R = Relative water content of leaf (%).

2.2.4. Statistical analysis

Minitab v18 software was used to perform two-way ANOVA, correlation analysis, cluster analysis, principal component analysis (PCA), and multiple linear regression (MLR) analysis. The data were analyzed using two-way ANOVA to determine the significance of variation in air pollutants across seasons and locations. The ANOVA analysis also aided in determining the seasonal variation of the selected tree species' physiological and biochemical properties in the study region. Correlation analysis between different air pollutants and plant properties aided in determining the effect of air pollutants and particulate metal contents on various plant properties, whereas cluster analysis aided in determining the similarity between different groups of pollutants. PCA analysis was used to identify the source and the primary pollutant and plant property. The MLR model was created to determine the impact of air pollutants and particulate metal contents on plant APTI.

2.3. Result and discussion

2.3.1. Air pollution

The study sites chosen for determining the Dhanbad district's air quality index include two regions near coal mining sites (Jharia and Chasnala), one residential site (Digwadih), and one control site (Baramuri). Baramuri has a significant amount of forest and may be considered an ecologically sensitive region. As shown in Fig. 2.2, the Baramuri region had the least amount of all the air pollutants monitored (PM10, PM2.5, SO2, and NOX), whereas Jharia and Chasnala had very high pollutant levels. All sites with the highest AQI levels had the highest levels of air pollutants, PM10, PM2.5, and SO2, during the premonsoon season. Except for Baramuri, all of the sites' PM10 and PM2.5 concentrations were higher than the CPCB standard concentrations (100 g/m³ and 60 g/m³, respectively) in the premonsoon season, during the monsoon, and after the monsoon. SO2 concentrations were highest during the premonsoon season and lowest during the monsoon season, but NOX concentrations were highest during the postmonsoon season for all sites except Jharia. Jharia is well-known for its underground mine fires. As a result, the higher level of NOX in Jharia during the premonsoon season (111.28 g/m³) is understandable. Jharia (95.45 g/m³) and Digwadih (100.17 g/m³) SO2 levels exceed the CPCB standard limit (80 g/m³). Except for Jharia during the premonsoon season, all sites have NOX levels lower than the CPCB standard (80 g/m³).

Figure 2.2  Seasonal variation in air pollutants of different sites in the Dhanbad district.

The Baramuri region's air quality index (AQI) indicates that it has moderate air quality during the premonsoon (79.70) and monsoon (53.11) seasons and good during the postmonsoon (46.45) seasons. The air quality in the Digwadih region was unhealthy for sensitive groups during the premonsoon season (120.13) but moderate during the monsoon (78.59) and postmonsoon (69.02). The AQI value of Jharia ranges from 207.06 during the premonsoon season to 199.63 and 114.26 during the monsoon and postmonsoon seasons, indicating the region's air quality is very unhealthy for sensitive groups, which is consistent with the observations of Mondal et al. (2020). Chasnala has slightly better air quality than Jharia (157.00, 96.22, and 199.43 for pre, monsoon, and postmonsoon, respectively). Nonetheless, the region's air quality is moderate to unhealthy for sensitive groups.

2.3.2. Heavy metals in PM

Cadmium (Cd), Cobalt (Co), Chromium (Cr), Copper (Cu), Iron (Fe), Manganese (Mn), Nickel (Ni), Lead (Pb), and Zinc (Zn) are the most common atmospheric metals observed in the Dhanbad region (Mishra et al., 2013; Pandey et al., 2014a; Mondal et al., 2020). In the current study, PM10 samples were analyzed for heavy metals in the atmosphere, which revealed that Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn are present in significant amounts in the PM10 of the study area, as shown in Fig. 2.3. All metals were found to be higher in the premonsoon season and lower in the monsoon season (except Cd). For all seasons, the mining regions of Jharia and Chasnala have the highest Cd, Fe, Mn, Pb, and Zn concentrations. In contrast, Digwadih has the highest Cr, Cu, and Ni concentrations, supporting the findings of Mishra et al. (2013) and Pandey et al. (2014a). For all seasons, Baramuri has the lowest metal content, with minimum values of Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn (in g/m³) of 0.05 (postmonsoon), 0.36, 0.51, 5.26, 0.11, 0.004, 3.90, and 2.91 (monsoon), respectively. Cd, Fe, Mn, Pb, and Zn concentrations were found to be 0.17, 14.81, and 11.29 g/m³ in Jharia during the premonsoon season, respectively, whereas Mn (0.20 g/m³) and Zn (4.16 g/m³) concentrations are highest in Chasnala during the premonsoon season. During the premonsoon season, the maximum concentrations of Cr, Cu, and Ni in Digwadih were 1.78, 2.67, and 0.027 g/m³, respectively. The average concentrations of Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn (in g/m³) are 0.12, 1.01, 1.73, 11.22, 0.16, 0.01, 7.79, and 3.57, respectively. The Central Pollution Control Board (CPCB) has set permissible limits for Pb and Ni in the atmosphere at 1 g/m³ and 0.02 g/m³, respectively (CPCB, 2009). The atmospheric Pb concentration exceeds the CPCB permissible limits in all sites and seasons. In contrast, the Ni concentration exceeds the CPCB permissible limit in Jharia and Digwadih during the premonsoon season. The Ni ambient air Ni concentration in Digwadih also exceeds during the postmonsoon season. In all seasons, atmospheric Cd and Cr exceed the USEPA permissible limits (0.0002 g/m³ and 0.012 g/m³, respectively) in all Dhanbad district sites (Morakinyo et al., 2021). Cu, Fe, Mn, and Zn are considered plant nutrients, and their concentration in the air of the Dhanbad region does not appear to be of concern.