# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 1 Introduction  
Mining activities are the cornerstone of modern civilization, as these provide essential resources for infrastructure, industry, and technology [1]. However, such benefits come at a significant environmental cost, as mining activities have significant environmental impacts, including the creation of solid, liquid, and gaseous wastes, leading to pollution and ecosystem degradation [2]. Among these environmental impacts, air quality stands as a critical issue due to its direct and demonstrably adverse effects on human health and ecological well-being [3, 4]. The release of particulate matter (PM) from mining operation areas is a significant source of dust emissions, primarily arising from activities such as blasting, material handling, processing, and transportation. These activities disturb settled particles, leading to their suspension in the air, and, once airborne, they can translate into environmental hazards and lead to respiratory risks in human populations [2, 5]. Additionally, wind erosion of waste disposal sites, such as waste rocks and tailings, also contributes to dust generation into the surrounding environment [6]. In recent years, the demand for low-grade iron ore has surged across domestic and export markets [7]. To meet this growing demand, iron ore mines often increase production, leading to a proportional increase in dust generation and associated air pollution [8]. Thisnecessitates a comprehensive assessment of the potential air quality impact arising from such production expansions. Environmental impacts associated with mining activities are broadly categorized into primary and secondary impacts [9]. Primary impacts directly result from the mining operations, while secondary impacts include broader societal and economic changes inflicted by the mining project. This study focused on the primary air quality impacts anticipated due to the planned production capacity at a specific iron ore mine (IOM) in the western part of India. Environmental Impact Assessments (EIAs) are crucial for evaluating the potential environmental consequences associated with various projects, including mining operations [10]. The air quality assessment is a key component of EIAs to ensure proper evaluation and implementation of control measures [11; 12]. Air pollution modeling plays a crucial role in understanding, assessing, and regulating air quality and the distribution of toxic pollutants. Mathematical modeling serves as a valuable tool for anticipating these impacts, filling the gaps in the traditional assessment methods and enhancing decision-making processes in mining activities [11; 13]. Established methodologies incorporating recent studies on iron ore-mine emissions and modeling have been used to model the air quality around such mining areas [14]. Researchers have primarily used models like AERMOD, CALPUFF, and HYSPLIT, which are effective for long-term and complex terrain assessments but less suitable for short-term predictions in flat terrains typical of many mining regions [14; 15]. This study is an attempt to bridge the gap by employing the ISCST3 model for accurate short-term predictions of particulate-matter dispersion in a flat-terrain setting. Various models, including Gaussian Dispersion Models and the Industrial Source Complex Dispersion Model, estimate pollutant concentrations from different sources during mining operations [15; 16]. These models help predict the horizontal and vertical dispersion of pollutants under different emission scenarios and meteorological conditions, aiding in the establishment of air quality-monitoring stations for effective management [17]. The ISCST3 model is employed to conduct a comprehensive assessment of the potential air quality ramifications arising from the proposed mine [17; 18]. In situations where mining is proposed, an opportunity exists to analyze the air quality impacts into two distinct phases, i.e., the construction phase and the operation phase [19; 20; 21].