Validation of AERMOD prediction accuracy for particulate matters (PM10, PM2.5) for a large coal mine complex: A Multisource Perspective

doi:10.21203/rs.3.rs-4023310/v1

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Validation of AERMOD prediction accuracy for particulate matters (PM10, PM2.5) for a large coal mine complex: A Multisource Perspective

https://doi.org/10.21203/rs.3.rs-4023310/v1

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published 12 Jul, 2024

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Particulate matter (PM) emission from coal mining activities is inevitable and a significant concern worldwide. American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is one of the most widely used dispersion models for predicting air PM dispersion in coal mines. However, validation of AERMOD-predicted PM concentration in a large mine complex has not been reported. So, in this study, AERMOD predicted PM concentration was validated against the PM concentrations measured by nine continuous ambient air quality monitoring stations (CAAQMS) stationed in the Singrauli coal mining complex. The complex contains nine coal mines across 438 square kilometers, with around 129 pollution sources chiefly from the area, pit, and line categories. PM₁₀ and PM_2.5 concentrations peak during summer (204.58 µg/m³) and winter (67.67 µg/m³), respectively. The AERMOD model predicts peak dispersion of PM₁₀ (500–1200 µg/m³) and PM_2.5 (100–800 µg/m³) during the winter season. The AERMOD model reveals that the region's wind movement caused by land and lake breezes was the predominant driver of PM surface dispersion. In the winter season, atmospheric inversion increases ground-level PM concentrations in the region. The AERMOD cannot represent the vertical dispersion of PMs in the summer, resulting in an underestimation of PM concentration. The statistical validation shows that AERMOD underestimates PM₁₀ and PM_2.5 concentrations across all seasons and years. The AERMOD model's prediction accuracy for PM₁₀ (R² = 0.38) and PM_2.5 (R² = 0.56) is also low. Finally, it can be concluded that AERMOD-predicted PM concentrations are not accurate for large mining complexes but more suitable for individual mines.

AERMOD

Dispersion Modeling

Coal mine

Particulate matter

Land breeze

Energy is the driving force behind human life and is critical to its evolution. The global need for energy is rapidly increasing as the world's population grows, urbanizes, and modernizes. Global energy demand has risen dramatically, from 8588.9 million tonnes of oil equivalent (Mtoe) in 1995 to 13147.3 Mtoe in 2015, with yearly consumption expected to reach 13865 Mtoe in 2023 (Ahmad & Zhang 2020; Ritchie et al. 2020; The World Counts 2024). Global energy consumption is expected to increase from 1–2% annually (Ibrahim 2021). Developing countries drive energy consumption, contributing to more than 100% of world demand growth (Scheffran et al. 2020). Fossil fuels contribute approximately 66% of energy demand (Ibrahim 2021). Coal provides around a quarter of the world's primary energy and two-fifths of its electricity, making it the most dominating fuel for power generation (IEA 2021). As a result, coal mining is critical to supplying global energy needs.

However, coal mining activities such as digging and excavation, drilling and blasting, loading and unloading of coal and overburden, heavy vehicle movements on haul roads, dragline operations, coal crushing in feeder breakers, mine fire, exposed pit faces, wind erosion, and exhaust of heavy earthmover machinery emit various pollutants into the air, water, and soil (Derakhshani et al. 2019; Ma et al. 2020; Iskandar 2020; Akcin 2021). Air pollution, in particular, is a significant concern due to the release of particulate matter (PM), sulfur dioxide (SO₂), nitrogen oxides (NO_X), carbon monoxide (CO), and methane (CH₄) into the atmosphere, which causes respiratory diseases, cardiovascular problems, and global warming. Even the Sustainable Development Goals (SDGs), prioritized air pollution, with two primary SDG objectives assigned: SDG 11.6 (mitigation of air pollution impacts on people in large cities) and SDG 3.9 (significant reduction in health effects due to hazardous elements) (Ahmad & Zhang 2020).

The atmospheric Particulate matter (PM) from coal mines contains dangerous metals that impair human health, including respiratory, cardiovascular, and cerebrovascular consequences and vegetation (Luo et al. 2021; Mishra et al. 2024). Dust and coal particles generated during mining and soot emitted during coal transport disseminate throughout the surrounding area. As a result, the dispersion of these PMs around coal mines is a significant concern. The American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is one of the most widely used dispersion models for studying air pollution dispersion near coal mines (Vora 2010; Chinthala & Khare 2011; Kundu & Pal 2018; Srivastava et al. 2021; Khazini et al. 2021; Peter & Nagendra 2021).

The application of AERMOD in the mining industry has presented challenges, particularly in modeling non-stack fugitive sources such as haul roads, in-pit mining activities, and material handling, which are common in coal mining operations and frequently overestimate or underestimate pollution levels (Srivastava et al. 2021; Khazini et al. 2021; Peter & Nagendra 2021). The AERMOD dispersion model assesses PM dispersion using surface meteorological factors such as wind direction, wind velocity, humidity, and temperature, as well as an emission inventory (De-Souza et al. 2014; Coria et al. 2015; Wang et al. 2017). However, past research on opencast coal mines has concentrated on PM dispersion from a single mining area.

In this study, AERMOD has been applied to check the dispersion of PM for a mining complex that includes 9 open cast coal mines. The AERMOD dispersion model is used in this work to examine the dispersion of PMs (PM₁₀ and PM_2.5) from the Singrauli mine complex on an annual and seasonal scale. Singrauli mine complex spans 438 square kilometers and includes nine distinct mines and 129 sources from area, line, and pit distributed across two districts, Sidhi and Sonbhadra. As a result, PM pollutants are likely to disperse across large distances, harming the population of these districts and those in neighboring districts. Seasonal fluctuation aids in projecting the critically affected period of the year and developing a recommendation for PM prevention and mitigation during critical times. This study aims to validate the prediction accuracy of particulate matter by AERMOD in the Singrauli mine complex, and this study also aims to segregate the pit, line, and area sources of dust in the mining complex.

2.1 Study Area

Singrauli mining complex has been selected as the study area (Fig. 1). Singrauli is recognized as India's energy hub, with several coal-fired thermal power stations and considerable coal mining activities (Romana et al. 2022). The Singrauli mining complex covers 438 square kilometers in two districts: Sidhi in Madhya Pradesh and Sonbhadra in Uttar Pradesh, India. Table 1 shows that the region has nine opencast mines. Singrauli mine complex is known for its vast coal reserves, and coal mining takes place in two sub-basins: Moher and Singrauli. The Moher Sub-Basin has about 6.83 billion tonnes of coal reserves, whereas the Singrauli Main Basin has around 3.23 billion tonnes (Javed et al. 2021). The main Singrauli basin is 1,890 square kilometers and lies further to the west. It is entirely unexplored.

The Singrauli region's geological features include sedimentary rock formations from the Vindhyan and Gondwana Supergroups, volcano-sedimentary rock formations from the Precambrian Mahakoshal Group, and the Precambrian Chhotanagpur Granite Gneiss Complex (CGGC). The CGGC, represented by the Dudhi group of rocks, comprises migmatitic granitic gneisses, porphyritic granite, and many metasedimentary enclaves (Romana et al. 2022). During the pre-industrialization era, the region was dominated by forest (43.35%), agriculture (38.56%), and cultivable wasteland (17.44%).

The Singrauli region is in a temperate zone, with a Köppen climate classification of Csa. The average annual precipitation is 1014 mm, with around 80% of it falling during the monsoon season, and the mean temperature is about 25.2°C, with temperatures as low as two °C in the winter and as high as 47°C in the summer (Romana et al. 2020 & 2022). However, the mine complex is scattered across a large area, and such areas have been shown to establish their microecosystem. The study area was divided into four quadrants based on the distance between each mine and the center of the mining complex to investigate the impact of the surrounding environment on the micro-climatic conditions of the mining complex. All large projects are in the third quadrat, whereas modest projects are in the fourth quadrat. A major mega-power plant project borders the complex.

Table 1

Name of Open Cast Coal Mining Project and Locations of Meteorological Station in Singrauli
Sl. No.	Open Cast Coal Mines	Latitude	Longitude	Quadrant
District-1	Singrauli (Madhya Pradesh)
1	AMLOHRI PROJECT	24° 05' 56.24" N	82° 36' 17.50" E	III
2	BLOCK-B PROJECT	24° 12' 18.68" N	82° 35' 30.88" E	II
3	CETI (DUDHICHUA) PROJECT	24° 12' 24.18" N	82° 40' 16.59" E	II
4	JAYANT PROJECT	24° 06' 56.00" N	82° 39' 24.00" E	III
5	JHINGURDA PROJECT	24° 11' 48.10" N	82° 42' 13.00" E	I
6	NIGAHI PROJECT	24° 06' 28.23" N	82° 37' 42.44" E	III
District-2	Sonbhadra (Uttar Pradesh)
7	BINA PROJECT	24° 09' 05.20" N	82° 46' 27.40" E	IV
8	KAKRI PROJECT	24° 10' 25.83" N	82° 45' 48.55" E	I
9	KHADIA PROJECT	24° 07' 20.00" N	82° 41' 04.20" E	IV

2.2 Data Collection and AERMOD modeling

The particulate matter (PM₁₀ and PM_2.5) data for 2020 were acquired from the Continuous ambient air quality monitoring stations (CAAQMS) installed by Northern Coalfields Limited (NCL). CAAQMS stations have been built 1.5 meters above ground level near all nine mines next to human settlements, and their locations are listed in Table 1. The CAAQMS stations monitor PM₁₀ and PM_2.5 at 15-minute intervals. Data backfilling was used to complete the missing data set, and the results were averaged to produce mean daily data. CAAQMS measures PM₁₀ and PM_2.5 using the beta ray attenuation technique, which includes measuring the absorption of energy going through a filter tape using a beta gauge.

In contrast, the meteorological data for 2020 needed for this analysis are derived from the LaGa systems. The meteorological data obtained are at hourly intervals. The daily data was obtained by first averaging them. The Weather Research and Forecasting (WRF) Model was employed to transform the data to AERMOD input format. The WRF data has a resolution of 4 km and a grid size of 50 km x 50 km. They are input for AERMET, a preprocessing program that organizes meteorological data before executing models. The parameters include opaque cloud cover (tenths), temperature (̊C), relative humidity (%), station pressure (millibar), wind direction (degree), wind speed (m/s), ceiling height (m), global horizontal radiation (Watt-hours/m²), and hourly precipitation (mm).

AERMOD View 10.0.1, a dispersion modeling software, calculates the ground-level concentration of pollutants using the Gaussian plume equation. This software identifies individual receptor locations and creates concentration contours depending on pollution sources (Khazini et al. 2021; Sahu et al. 2018). In the coal mine area, there are three primary sources of pollution: pit sources (volume sources or emissions from quarries), area sources, and line sources, as seen in Fig. 2.

Transportation activities that use haul roads are considered line sources in dispersion modeling. These sources are distinguished by emissions that follow a linear course (Kahraman & Erkayaoglu 2020). In contrast, loading, stockpiling, and unloading operations are classified as area sources, whereas drilling and excavation are classified as volume sources. Area sources produce pollutants over a specific region, contributing to total pollution dispersion (Srivastava et al. 2021). The Singrauli mine complex contains around 18 pit sources, 67 area sources (coal dumps, overburden (OB) dumps, and drilling benches), and 44 line sources (coal and OB transportation highways) (Table 2).

Table 2

Categorization of different Sources and their Description
Sl. no	Name of Sources	Categories	Number of Sources	Key Activities involved
1	Polygonal Area Sources	Over Burden Dumps	31	Unloading (OB), Dozing (OB), Wind Erosion
2	Polygonal Area Sources	Coal Dumps	18	Unloading (Coal), Loading (Coal), Wind Erosion
3	Area Source	Drilling Bench	18	Drilling, Blasting, Loading (OB)
3	Pit Source	Coal Quarries	18	Drilling (Coal Bench), Blasting, Loading (Coal), Wind Erosion
4	Line Sources	OB Transportation Road	20	Transportation (Drilling Benches to OB Dumps)
5	Line Source	Coal Transportation Road	24	Transportation (Coal from quarry to coal dumps)
Total Sources			129

The meteorological input is provided via the AERMET extension in "sfc" format, sourced from the WRF model. The preprocessing file also runs on upper air data, which refers to meteorological parameters in the vertical direction of the atmosphere, comprising the mixing height (m), atmospheric pressure (millibar), dry bulb temperature (degree Celsius), and relative humidity (%). The AERMET has an inbuilt upper air estimator that uses hourly ground meteorological data to estimate upper air data. In addition to upper air estimation, AERMET computes surface land characteristics such as albedo (surface reflectance), surface roughness, and the Bowen ratio, which is the ratio of sensible to latent heat flux, based on the land use details provided and their variation over time (annual, seasonal, or monthly) in the modeled area. Figure 3 depicts the land use information for the entire mining complex as pitched by the preprocessor.

Another AERMOD preprocessor, AERMAP, processes the terrain file to derive base elevation and terrain elevation for the sources, buildings, and receptors under consideration for the study. It also helps to generate 2D and 3D images of the surrounding terrain. The Shuttle Radar Topography Mission (SRTM1) with a 30 m resolution and WGS84 datum is used in the investigation. This dataset can be loaded directly using the software's inbuilt AERMAP. Once the preprocessors AERMET and AERMAP have been completed, the following input parameters, i.e., pollution sources, receptors (locations where the concentration is analyzed), and categories of pollutants, are entered into the AERMOD software for dispersion modeling.

2.3 Emission Rate Calculation

The emission rate is an essential input parameter for source information in AERMOD, and it changes depending on the types of sources and the essential activities related to mining (Table 2). The OB dumps have unloading and dozing of OB. Wind also plays a significant role in the spread of dust from dumps. Furthermore, loading coal into loaders and conveyors for further transportation increases dust emissions from coal dumps. The loading of OB from drilling benches and coal from quarries significantly impacts overall emissions. Unpaved roads used to transport OB and coal from drilling benches to OB landfills and quarries to stockyards contribute significantly to overall particle pollution emissions. The emission rates for PM₁₀ and PM_2.5 are computed using US Environmental Protection Agency (EPA) components and formulae (Table 3). The emission rate is computed separately for each area's primary operations, pit and line sources (129 sources), and all nine mines in the complex.

Table 3

Emission factors used for emission rate calculation for different sources in coal mines
Sl. No.	Activities	Unit	PM₁₀	PM_2.5	Equations	References
1	Drilling in Coal Bench	kg/hole	0.22	0.04	E = (((0.3 * A₁) / H_a) * E_dc)	S&T Project (EE-27) EIA-EMP (CMPDI) (2002–2008)
2	Drilling in OB Bench	kg/hole	0.56	0.11	E = ((A₂ / H_a) * E_do)
3	OB Loading	kg/t	0.00014	0.000015	E = E_lo* L_o
4	OB Unloading	kg/t	0.0005	0.00006	E = E_uo* L_o
5	Coal Loading	kg/t	0.0015	0.00021	E = E_lc * L_c
6	Coal Unloading	kg/t	0.00123	0.00014	E = E_uc* L_c
7	Coal / OB transportation on the unpaved haul road	kg/VKT	0.53	0.076	E = ((C_p / T_s) * 2 * R) * E_t	MoEFCC (2018)
8	Blasting OB / Coal	kg/ blast	0.0106	0.000727461	E = (((0.3 * A₁) / H_a) * E_b) E = ((A₂ / H_a) * E_b)	(AP 42) USEPA (1998)
9	Dozing	kg/hr	0.8584	0.3256	E = E_do * 4
10	Wind erosion from OB dumps, coal mine pits, and coal stockyard	kg/ha/hr	0.09	0.008	E = A_w * 8 * E_w
VKT = Vehicle kilometers Travelled

E = Emission rate of the key activities in (Kg/year)

A₁ = Available area of coal bench for drilling (m²)

A₂ = Available area of OB bench for drilling (m²)

H_a = Hole area dimension (L = 4.5 m, W = 4 m)

E_dc = Emission factor of drilling coal bench (kg/hole)

E_do = Emission factor of drilling OB bench (Kg/hole)

E_lo = Emission factor of loading OB (kg/t)

E_uo = Emission factor of unloading OB (kg/t)

E_lc = Emission factor of loading coal (kg/t)

E_uc = Emission factor of unloading coal (kg/t)

L_o = Active area of OB dumps (m²)

L_c =Active area of Coal stockyard (m²)

C_p = Coal Production (Tonnes)

T_s = Vehicle Capacity (60 tonners for coal, 100 tonners for OB)

R = Road Length (Km)

E_t = Emission factor of coal and OB transportation (Kg/VKT)

E_b = Emission factor of blasting coal/OB (Kg/Blast)

E_do = Emission factor of Dozing (kg/hr)

E_w = Emission factor for wind Erosion (kg/ha/hr)

A_w = Available area for wind erosion of coal Stockyard/ OB dumps (ha)

The computed emission rate in Kg/year is then translated to g/s, assuming 330 working days per year before entering the AERMOD program. The average release height for emissions from unpaved roads is estimated to be 7 meters, whereas for pits, it is roughly 25 meters from the base level. The release height of pollutants is the vertical distance they travel from their source. The double-lane road's typical width is 40 m, with a vehicle height of 4.3 meters. The soil's density is calculated to be 2.7 kg/m³. Based on calculations, the resultant emission rate of the Singrauli mining complex is 10.85 g/s of PM₁₀ and 2.32 g/s of PM_2.5.

The AERMOD model is then run simultaneously for all 129 sources in the mining complex to create annual 24-hour concentration contours for PM₁₀ and PM_2.5. The seasonal contours for PM₁₀ and PM_2.5 are constructed by running the model for each of the four seasons individually. The winter, pre-monsoon, monsoon, and post-monsoon seasons span from January to February, March to May, June to September, and October to December, respectively. The annual 24-hour predicted concentration from AERMOD predicted concentrations of PM were validated against the observed annual average CAAQMS data from the mine complex. In contrast, the seasonal 24-hourly predicted concentrations are checked against the mining complex's average CAAQMS data. The model uses CAAQMS stations as receptors to track 24-hour ground-level concentrations.

2.4 Model Performance Evaluation

AERMOD model performance is assessed using statistical performance metrics such as the Index of Agreement (d), Coefficient of determination (R²), Model Bias (MB), Normalised Mean Square Error, and Fractional Bias (FB) (Chang & Hanna 2004; Hadlocon et al. 2015; Huertas et al. 2012; Rood 2014). Table 4 shows the equations for calculating these metrics and the range and optimum values.

Table 4

Performance Parameters Used for Model Validation
Performance Parameters	Formula	Acceptable Range	Optimum Values
R²	\(\text{R}=[1-\frac{{\sum }_{\text{i}=1}^{\text{N}}{\left({\text{O}}_{\text{i}}-{\text{P}}_{\text{i}}\right)}^{2}}{{\sum }_{\text{i}=1}^{\text{N}}{\left({\text{O}}_{\text{i}}-\underset{\_}{\text{O}}\right)}^{2}}]\)	-	1
NMSE	\(\text{N}\text{M}\text{S}\text{E}=\frac{\underset{\_}{{\left({\text{O}}_{\text{i}}-{\text{P}}_{\text{i}}\right)}^{2}}}{\underset{\_}{\text{O}}*\underset{\_}{\text{P}}}\)	< 1.5	0
FB	\(\text{F}\text{B}=2\left(\frac{\underset{\_}{\text{O}}-\underset{\_}{\text{P}}}{\underset{\_}{\text{O}}+\underset{\_}{\text{P}}}\right)\)	-0.3 to + 0.3	0
MB	\(\text{M}\text{B}=\underset{\_}{{\text{P}}_{\text{i}}-{\text{O}}_{\text{i}}}\)	-	0
d	\(\text{d}=1-\frac{{\sum }_{\text{i}=1}^{\text{N}}{\left({\text{P}}_{\text{i}}-{\text{O}}_{\text{i}}\right)}^{2}}{{\sum }_{\text{i}=1}^{\text{N}}{\left[\left\|{\text{P}}_{\text{i}}-\underset{\_}{\text{O}}\right\|+\left\|{\text{O}}_{\text{i}}-\underset{\_}{\text{O}}\right\|\right]}^{2}}\)	-	1

\({\text{P}}_{\text{i}}\) is the predicted data points (from AERMOD), N is the number of data points,\(\underset{\_}{\text{O}}\) is the mean observed data points (obtained from CAAQMS),\({\text{O}}_{\text{i}}\) is the number of observed data points, and\(\underset{\_}{\text{P}}\)is the mean predicted data points.

3.1 Microclimatic Condition in the Mining Complex

To better understand the microclimate of the Singrauli mining complex, meteorological characteristics such as minimum and maximum temperatures, wind speed, relative humidity, solar radiation, wind direction, and rainy days are analyzed for each mine (Table 5). Meteorological data collected from stations within each mine show that the yearly average rainfall for the mine complex is 0.0091 mm/day, with approximately 38 rainy days. The mining complex has an annual minimum temperature of 7.17 ºC and a maximum temperature of 45.08 ºC, with a significant variance. The low annual mean precipitation and high-temperature range indicate that the region has a hot and arid climate, primarily during the monsoon season (Li et al. 2010; Garg et al. 2016; Raja & Reddy 2019). The area experiences long periods of drought and water scarcity, which can have severe consequences for the local ecosystem, agriculture, and water resources, implying that the region is prone to desertification, which can exacerbate the arid conditions (Li et al. 2010; Chowdari et al. 2015).

Table 5

Meteorological Characteristics of the Studied Area
Sl. No	Meteorological Parameters	Amlohri	Blockb	Dudhichua	Jayant	Jhingurda	Nigahi	Bina	Kakri	Khadia
1	Minimum temperature (^oC)	6.95 (Jan)	7.34 (Jan)	6.73 (Jan)	8.02 (Jan)	3.94 (Feb)	7.78 (Jan)	8.73 (Jan)	7.15 (Jan)	7.87 (Jan)
2	Maximum temperature (^oC)	44.72 (May)	45.91 (May)	44.16 (May)	45.52 (May)	44.63 (May)	45.63 (May)	44.36 (May)	45.8 (May)	44.99 (May)
3	Minimum Wind Speed (m/s)	0.01 (Oct)	0.01 (Oct)	0.01 (July)	0.01 (Aug)	0.01 (Aug)	0.01 (Oct)	0.01 (Mar)	0.02 (Jan)	0.01 (Mar)
4	Maximum Wind Speed (m/s)	6.68 (April)	0.9 (Jan)	3.51 (Nov)	2.92 (May)	2.14 (Aug)	3.48 (May)	3.60 (May)	2.16 (Mar)	5.19 (Jan)
5	Wind Direction (Degree)	0-360	0-360	0-360	0-360	0-360	0-360	0-360	0-360	0-360
6	Minimum Relative Humidity (%)	14.67% (May)	10.4% (May)	10.03% (May)	4.85% (May)	4.6% (April)	10.67% (May)	12.84 (May)	13.24 (May)	8.11 (May)
7	Maximum Relative Humidity (%)	100% (June)	100% (July)	100% (Sept)	100% (Sept)	100% (July)	100% (Oct)	100% (June)	100% (June)	97.21% (Sept)
8	Number Of Rainy Days	37	42	41	36	31	31	33	48	39
9	Minimum Global Horizontal Radiation (W/m²)	11.25 (July)	19.14 (June)	15.07 (April)	17 (June)	3.67 (Jan)	18.1 (June)	18.5 (Mar)	7.69 (June)	2.57 (Dec)
10	Maximum Global Horizontal Radiation (W/m²)	971 (May)	357 (June)	435.53 (Sept)	387.6 (Sept)	499.87 (March)	502.26 (May)	502.4 (May)	404.76 (June)	878.08 (Aug)

The monsoon season has the highest relative humidity and the lowest global horizontal radiation. The mining complex's yearly mean relative humidity is around 69.23%, and the minimum and maximum global horizontal radiation are 12.55 W/m² and 548.72 W/m², respectively. As a result, the inference of a hot and arid environment with high moisture content in the mining complex's air is strengthened. The maximum and minimum wind speeds are 3.40 m/s and 0.01 m/s, respectively. The wind direction is predominantly northwest (Fig. 4). The region's high atmospheric moisture can be ascribed to wind movement from the surrounding largest artificial lake, Govind Ballabh Pant Sagar (GBPS) (Beersma & Buishand 2003; Panda & Sahu 2019; Lone et al. 2022). The GBPS is positioned in the southeast, which explains the major wind movement in the northwest direction caused by the lake breeze from the water body to the arid mine complex.

Jhingurda mine has the lowest temperature (~ 4ºC) in February, while Block B has the highest temperature (~ 46ºC) in May. Amlohri experiences the highest wind speed (6.68 m/s in April), highest global horizontal radiation (971 W/m² in May), and lowest relative humidity (14.67% in May). In contrast, Khadia has the lowest global horizontal radiation (2.57 W/m² in December) and the rainiest days (48). The Jhingurda and Block B mines are located at higher elevations and farther away from the water body, resulting in extreme temperatures caused by external forces. On the contrary, since Khadia and Kakri are located near water bodies, aerosols (including cloud condensation nuclei) are projected to be highest above these mines, enhancing cloud cover and decreasing global horizontal radiation (Shrestha 2019). Amlohri is located in the southwest portion of the mine complex. The wind direction is the least prominent, while the wind speed is the highest. As a result, moisture-laden air does not enter the mine, reducing relative humidity, aerosols, and cloud cover. At the same time, the clear sky permits the location to receive the highest global horizontal radiation (Shrestha 2019).

Wind speed and direction are the main influences on complex microclimatic conditions in mining. As a result, the mining complex is separated into four quadrants for further examination of these variables. Jhingurda and Kakri, Block B and Dudhichua, Jayant, Nigahi, Amlohri, and Bina and Khadia mines are located in quadrants I, II, III, and IV, respectively. Quadrants I and IV are located near the lake, with Quadrant I's mines at higher elevations. As a result, the breeze from the lake is primarily directed towards the dry land in quadrant IV. However, the land breeze towards the lake can also be seen blowing down the slope. Quadrant IV is located at depths, and the wind generally moves south and southwest after returning from higher elevations from the north. During chilly weather, the land breeze flows in an easterly direction. Quadrat II is the farthest part from the lake and is located at a substantial depth. As a result, the wind moves primarily in the north, northwest, and western directions. The coal mines in quadrat III are located at higher elevations and steadily increase towards the south. As a result, the lake breeze movement is primarily northwest, whereas the land breeze flow is eastward. As a result, the wind's overall direction of flow is northwest.

3.2 Particulate Pollution Status of the Mining Complex

The annual PM₁₀ and PM_2.5 concentrations at the Singrauli mining complex are depicted in Fig. 5. The mining complex's PM₁₀ concentration varies from 14.18 µg/m³ in August to 204.58 µg/m³ in March, with an annual average of 91.18 µg/m³. The average annual PM₁₀ concentration in Amlohri, Dudhichua, Jhingurda, and Nigahi exceeds the Central Pollution Control Board's (CPCB) permissible limits of 100 µg/m³, while all mines, except Bina, exceed the WHO standard limits of 45 µg/m³ (CPCB 2010; World Health Organisation 2021). PM_2.5 concentrations at the mining complex vary from 5.57 µg/m³ in July to 67.67 µg/m³ in February, with an annual average of 27.89 µg/m³. All mines, except Dudhichua, have PM_2.5 concentrations below the CPCB's permitted limit of 60 µg/m³ (CPCB 2010). However, PM_2.5 concentrations in all sites surpass the WHO recommended limits of 15 µg/m³ (World Health Organisation, 2021). Amlohri has the most significant annual mean PM₁₀ concentration (152.75 µg/m³), whereas Dudhichua has the highest annual mean PM_2.5 concentration (49.64 µg/m³) across all sites.

Amlohri and Dudhichua are in quadrats III and II, respectively. Both places are on the far side of the lake. Because PM₁₀ particles are heavier than PM_2.5 particles, the breeze from the lake moving south and southwest is more likely to carry and deposit them near the Amlohri mining site. However, the lighter PM_2.5 particles are expected to move farther than the PM₁₀ particles in the north and northwest and deposited at lower elevations in Dudhichua. The higher altitudes in the Block B region cause the wind to deposit PMs in the Dudhichua region before further moving to the northwest (Seastedt et al. 2004).

The seasonal variation in PM₁₀ and PM_2.5 concentrations (Fig. 5) demonstrates that PM concentrations in the region are very high during dry months, summer, and winter, confirming transboundary movement of atmospheric particulates under the influence of sea breeze and land breeze conditions (Varaprasad et al. 2024). Summer PM₁₀ concentrations can reach 210 µg/m³, while winter PM_2.5 concentrations peak at 80 µg/m³. The monsoon season has the lowest PM concentration due to the wet deposition process, while the concentration gradually increases after the monsoon season (Wu et al., 2018). The post-monsoon PM_2.5/PM₁₀ concentration ratio is very high. The high moisture content and low gust limit the smooth transport of larger air particulates (Jones & Harrison 2004). PM₁₀ particulates tend to agglomerate and create larger particles, which settle down or act as cloud condensation nuclei, forming aerosols (Wu et al. 2018). As a result, the atmospheric particulates transported by the air during the post-monsoon season are typically PM_2.5 particles.

3.3 AERMOD Dispersion Modeling

AERMOD simulates the effects of ground-level and elevated industrial sources on flat or rather complex terrain (ul Haq et al. 2019). AERMOD has been utilized in Indian geo-mining circumstances to assess PM concentrations in opencast mines and estimate dust concentrations at receptor sites (Kundu & Pal 2018; Sahu et al. 2018; Srivastava et al. 2021; Srivastava & Elumalai 2021). AERMOD predicts wind-driven PM dispersion by combining air dispersion based on planetary boundary layer turbulence structure and scaling ideas, including consideration of surface and elevated sources and simple and complicated topography (ul Haq et al. 2019). However, AERMOD has been shown to overestimate fugitive dust concentrations when applied, particularly in low wind speed conditions, and to underestimate in non-Gaussian vertical dispersion conditions (Huertas et al. 2012; Kundu & Pal 2018; Sahu et al. 2018; Srivastava et al. 2021; Srivastava & Elumalai 2021). The height of mine benches influences PM dispersion at higher depths in opencast mines. Particulate matter formed on a mine's lower benches moves up to the higher benches before exiting the mine (Gautam & Patra 2015). AERMOD describes non-Gaussian vertical dispersion under convective conditions, distinguished by updraft and downdraft motions with varying probabilities (Snoun et al. 2023).

The current AERMOD dispersion model forecasts yearly and seasonal PM₁₀ and PM_2.5 dispersion from all Singrauli mining sources (Figs. 6 and 7). The grid size is 100m × 100m, with nine distinct receptors at each CAAQMS station. Exactly 129 sources of PM were found. Jayant mine has the highest yearly AERMOD predicted PM₁₀ concentration of 1461 µg/m³, whereas Jhingurda mine has the lowest value (< 300 µg/m³). Amlohri, Nigahi, and Jayant mining sites have the most significant annual PM₁₀ concentration (> 1400 µg/m³), followed by Khadia, Block-B, and Kakri mines (~ 1000 µg/m³).

In mining locations, the winter season has the highest AERMOD predicted PM₁₀ levels (> 1200 µg/m³). The cold weather conditions cause air inversion, which reduces PM₁₀ dispersion. In contrast, PM₁₀ concentrations around mining projects are less than 800 µg/m³ throughout the summer. The concentration of PM₁₀ over residential sites is also much lower in the summer than in the winter, demonstrating that particulates are widely dispersed. The monsoon season has the lowest PM₁₀ concentrations in the region, with levels below 600 µg/m³ for mining sites and 400 µg/m³ at residential locations. Wet deposition through rainfall appears to reduce PM₁₀ concentrations in the atmosphere considerably. When the rain stops, the concentration of PM₁₀ rises rapidly during the post-monsoon season. PM₁₀ concentrations can reach over 1000 µg/m³ over mining sites and 400–800 µg/m³ over residential areas.

In contrast, the annual PM_2.5 contamination is evenly spread throughout all mines except Jhingurda. Khadia mine has the highest yearly PM_2.5 concentration (863 µg/m³), whereas Jhingurda mine has the lowest annual PM_2.5 pollution (< 60 µg/m³). The Dudhichua project appears to have considerably impacted the region's PM_2.5 concentration. However, the receptors at the project's residential site are oblivious to the contamination. PM_2.5 accumulates extensively above mining projects throughout the winter season and dissipates away during the summer season, similar to PM₁₀. During winter, PM_2.5 levels over the Khadia project can exceed 800 µg/m³. In winter, residential areas near the Khadia mine experience high levels of PM_2.5 contamination, reaching up to 500 µg/m³. Other mining sites have high PM_2.5 concentrations ranging from 100 to 400 µg/m³. The decreased concentration of PM_2.5 during the summer months also reflects the pollutant's dispersion in warm air conditions.

During the monsoon season, PM_2.5 concentrations decrease significantly in all locations, including mining sites, where they can dip below 100 µg/m³. However, the post-monsoon season displays a significant increase in PM_2.5 concentration. PM_2.5 pollution levels in Khadia, Dudhichua, and Jayant projects range from 300 to 600 µg/m³, whereas Amlohri and Nigahi projects range from 200 to 300 µg/m³.

The mining projects are primarily located in quadrants III and IV, and the PMs typically travel south to them. The sloping topography south of the mining projects likely aids in the downhill migration of particles. The predominant wind movement in the northwest direction causes the flow of PM dispersion; however, the air appears to dispose of the PMs over the mining projects before going further northwest and carries only a tiny fraction of the PMs. As a result, PMs moved from the residential areas south to the mining projects. The PM, concentration over GBPS, is similarly notably high, demonstrating dust deposition over the water body caused by the land and lake breeze phenomenon.

The wind's south and south-western flow in quadrant IV prevents pollution from spreading over the Jhingurda and Dudhichua project residential sites, making these areas among the least contaminated in the research area. PM_2.5 particles can travel farther northwest than PM₁₀. In quadrant IV, wind movement in the east, south, and southwest directions disperses PM_2.5 over the GBPS and residential areas south of the mining complex. Wind movement in quadrant III's northwest direction and north, west, and northwest directions in quadrant II spreads PM_2.5 particles in these directions. The wind blows eastward in quadrants III and IV, sweeping the PMs over the GBPS on the southeast side of the mining complex.

The high PM levels during the winter and post-monsoon season demonstrate that the land breeze is the more effective phenomenon in the region, resulting in widespread dispersion of particulates in the southeast direction of the mining complex during cold weather conditions. The dispersion of PMs over the GBPS is seen in cold weather. As a result, there is an urgent need to perform suspended and dissolved particulate analysis on the GBPS lake. Airborne particles are also known to transport hazardous metals (Mishra et al. 2024). As a result, the water body is likely to have high concentrations of these harmful elements. Soler et al. (2011), Mavrakou et al. (2012), and Reddy et al. (2023) have also demonstrated that the land breeze-sea breeze phenomena contribute significantly to air particulate dispersion over a region.

3.4 Statistical validation

The AERMOD dispersion models for PM₁₀ and PM_2.5 were validated using statistical methods. Model bias (MB), normalized mean square error (NMSE), and fractional bias (FB). Table 6 shows the values of the statistical validation techniques for PM₁₀ and PM_2.5. The index of agreement (d) can detect additive and proportional differences in observed and simulated means and variances (Duveiller et al. 2016). In contrast, the Coefficient of determination (R²) measures a model's goodness of fit and provides information about how well the data fits the model (Chicco et al. 2021). Model bias (MB) is the systematic departure of model predictions from actual values, indicating overestimation or underestimation, and reflects the degree of prediction error (Ato García et al. 2008; Duveiller et al. 2016; Valbuena et al. 2019). Fractional bias (FB) refers to the model's tendency to overestimate or underestimate observed values (Webster & Thomson 2022). The normalized mean square error (NMSE) is a model's accuracy as a function of data variation (Moradi et al. 2019). These statistical techniques assist in determining the model's performance and correctness.

Table 6

Statistical validation performance computed for the AERMOD model
Duration	d	MB	FB	NMSE	R²
PM₁₀
Winter	0.84	207.90	-0.95	3.36	0.46
Summer	0.94	136.38	-0.70	2.98	0.25
Monsoon	0.89	115.16	-1.08	4.19	0.59
Post-Monsoon	0.98	19.26	-0.17	2.01	0.45
Year	0.58	131.11	-0.77	2.90	0.37
PM_2.5
Winter	0.99	19.72	-0.34	0.59	0.70
Summer	0.98	18.53	-0.40	1.21	0.50
Monsoon	0.98	17.57	-0.57	1.46	0.65
Post-Monsoon	0.99	2.67	-0.07	1.52	0.46
Year	0.81	17.21	-0.38	1.04	0.56

The d (0.58) and R² (0.37) values of the yearly PM₁₀ model are shallow, while the MB (131.11) is significantly high. The FB value is -0.77, and the NMSE is 2.90. As a result, the AERMOD model has low prediction accuracy and a substantial negative error when estimating annual dispersion. As a result, the annual model severely underestimates the area's PM₁₀ dispersion. In contrast, the yearly PM_2.5 models show a low R² value (0.56) and a high d (0.81). The MB (17.21) and NMSE (1.04) are relatively low. However, FB is less than the permitted value (-0.38). Consequently, the AERMOD model offers moderate forecast accuracy for annual PM_2.5 dispersion. The AERMOD model underestimates the yearly PM_2.5 concentration with a significant margin of error for the entire mining complex.

The seasonal dispersion models of PM₁₀ and PM_2.5 show very high d values, ranging from 0.84 in winter to 0.98 in post-monsoon and 0.98 in summer and monsoon to 0.99 in winter and post-monsoon. However, the R² values of the PM₁₀ dispersion models vary from 0.25 in the summer to 0.59 during the monsoon. The models' NMSEs are also greater than 2.00, and except for the post-monsoon season, the PM₁₀ dispersion models show extremely high MB and FB values throughout the year. As a result, except for the post-monsoon period, the AERMOD model has low accuracy in estimating the seasonal dispersion of PM₁₀. The error variance is substantial throughout the year, and the models consistently underestimate PM₁₀ concentrations.

On the contrary, the AERMOD seasonal model's MB and NMSE values for PM_2.5 are significantly low, ranging from 2.67 in post-monsoon to 19.72 in summer and 0.59 in winter to 1.52 in post-monsoon, respectively. The dispersion model for winter has an R² value of 0.70. However, R² values in other seasons are much lower (0.46–0.65). In contrast, the MB for post-monsoon is relatively low (2.67), and for other seasons is likewise significantly low (< 20.00). The FB for post-monsoon is extremely low (-0.07), well within the optimal range. However, the FB during other seasons ranges from − 0.34 in winter to -0.57 in monsoon, which is outside the optimal range. Hence, the PM_2.5 dispersion modeling is relatively accurate during the winter and post-monsoon seasons. The post-monsoon error frequency is relatively low, while the error variance is lowest during winter. The dispersion model is more reliable during the winter than during the other seasons. As a result, PM_2.5 models outperform PM₁₀ dispersion models, while AERMOD dispersion models outperform thresholds set by Teggi et al. (2018) and Borrego et al. (2016).

The AERMOD dispersion models of PMs from Jharia coalfields and Kulda surface mine in India, Sungun copper mine in Iran, and opencast coal mines in Colombia have also shown similar d, R², FB, and NMSE values (Huertas et al. 2012; Sahu et al. 2018; Kundu & Pal 2018; Srivastava et al. 2021; Srivastava & Elumalai 2021; Khazini et al. 2021). The d, R², FB, and NMSE values of the AERMOD models over Indian mining settings are 0.34 to 0.93, 0.37 to 0.94, -0.08 to 0.63, and 0.07 to 0.67, respectively. The results are more accurate with a more minor modeling source and scope. Compared to AERMOD models outside of Indian mining circumstances, the PM dispersion models appear slightly better (Huertas et al., 2012). Outside of mining contexts, PM dispersion models demonstrate even higher forecast accuracy (Peter & Nagendra 2021).

The AERMOD predicted PMs were tested against data from the area's CAAQMS data, as shown in Fig. 8. The predicted vs. observed graph allows us to evaluate model performance across an unknown dataset in real-time (Sergeev et al. 2024). The R² value from the plot aids in measuring prediction accuracy and precision. The R² for the observed vs expected graphs of PM₁₀ and PM_2.5 is 37.63% and 56.36%, respectively. As a result, the PM₁₀ model fails to predict PM₁₀ concentrations, while the PM_2.5 model has poor prediction accuracy for the overall mine complex.

Coal mining emits a massive amount of PMs into the surrounding environment through various methods. The current study used 129 sources to investigate the dispersion of PMs in and around the Singrauli mining complex. The highest levels of PM₁₀ and PM_2.5 pollutants were detected during the summer and winter, respectively. However, the AERMOD model indicated that PM₁₀ and PM_2.5 concentrations will disperse most during winter. The statistical evaluation shows that the AERMOD model consistently underestimates PM₁₀, highlighting the approach's poor prediction accuracy. The AERMOD models have even been shown to underestimate PM₁₀ concentrations in all seasons, reducing yearly PM₁₀ concentrations in the region. The AERMOD models also underpredict PM_2.5 concentrations across all seasons. The AERMOD estimates for PM₁₀ were only somewhat correct during the winter and PM_2.5 throughout the winter and post-monsoon seasons.

The AERMOD models do not account for non-Gaussian vertical dispersion of pollutants. However, in warm climates (particularly in summer), PMs appear to scatter and pollute the surrounding air vertically, raising actual PM₁₀ and PM_2.5 concentrations in the air many times. As a result, the AERMOD model significantly underestimates the PM concentration for a large region.

Acknowledgments

The Authors wish to thank Coal India Limited for funding this project (project code: CIL/R&D/05/02/2021) and special thanks to Northern Coalfield Limited and Central Mine Planning & Design Institute for their technical and logistic help.

Statements and Declarations

The authors declare that they have no competing interests.

Funding

The research was funded by Coal India Limited under the project code number CIL/R&D/05/02/2021.

Availability of data and materials

The datasets generated during and analyzed during the current study are not publicly available as the data is collected from the concerned industry with their permission. The dataset is part of the Ph. D thesis but is available from the corresponding author upon reasonable request.

Software and data availability

Name of software: AERMOD VIEW 10.0.1

Year first available: 1996

Developers: Lakes Environment Software

License: Licensed Version (Serial #: AER0010836)

Hardware and software requirements: An Intel Pentium 4 processor (or equivalent) or higher, at least 2 GB of available hard disk space, 1 GB of RAM (2 GB recommended)

Consent for publication

Not applicable

Ethics approval and consent to participate

Not applicable

Authors’ contribution

Dr. Tanushree Bhattacharya communicated and coordinated this research work, Navin Prasad and Akash Mishra carried out the tests, and data analysis and drafted the manuscript. Dr. Bindhu Lal conceived of the study and participated in research coordination. The authors read and approved the final manuscript.

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published 12 Jul, 2024

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Editorial decision: Major revisions
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Validation of AERMOD prediction accuracy for particulate matters (PM10, PM2.5) for a large coal mine complex: A Multisource Perspective

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Journal Publication

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1 Study Area

2.2 Data Collection and AERMOD modeling

2.3 Emission Rate Calculation

2.4 Model Performance Evaluation

3. Result and Discussion

3.1 Microclimatic Condition in the Mining Complex

3.2 Particulate Pollution Status of the Mining Complex

3.3 AERMOD Dispersion Modeling

3.4 Statistical validation

4. Conclusion

Declarations

References

Status:

Journal Publication

Version 1