Economic Valuation of PM10 Emission Reduction Strategies in Mining: A Case Study of a Limestone Quarry in Thailand

Economic Valuation of PM10 Emission Reduction Strategies in Mining: A Case Study of a Limestone Quarry in Thailand

Tochukwu Ambrose Ngwu.

Department of Mining and Petroleum Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.

Abstract – Mining operations are crucial for socio-economic development as they provide vital raw materials for infrastructure, manufacturing and construction. Mining operations, however, are also significant contributors of particulate emittance especially coarse particulate matter (PM) produced during blasting, haulage and mineral processing. Studies have evaluated the concentration of the particulate and its environmental impact around the mining areas, however, the lack of emphasis on the economic valuation of emission control often limits the ability of mine operators and policy makers to prioritize investment and incorporate environmental performance in the operational decisions. In a bid to fill this gap, the research applies emission-factor-based inventory estimated using the United States Environmental Protection Agency AP-42 framework to determine the PM emissions of the major operational activities using a limestone quarry in Thailand. Emission under blasting, haul road transport, and mineral processing scenarios was measured using operational data obtained in Environmental Impact Assessment reports under uncontrolled and controlled scenario. Findings indicate that total PM emissions dropped to 117.97 t yr¹ from 278 t yr¹ (pre-mitigation), which is a 57.6% reduction with 89% of the total emissions attributed to haul-road transport. The economic benefit of 98.4 million is obtained by monetizing the avoided emissions at a social cost value of particulate pollution. By converting the emission cuts into financial units, the study shows how the emission-factor inventories can fill the disconnect between environmental performance and economic analysis, offering a viable decision-support model of operational optimization and strategic environmental investment in the mining industry.

Keywords PM emissions; Environmental impact assessment; Economic valuation; Environmental Protection; Mining; Particulate Matter

1. INTRODUCTION

   Mining is of major industrial importance in the global economy as it provides raw materials that are necessary in the infrastructural, manufacturing, and construction aspects. Mineral extraction is one of the major elements of the national economy of Thailand as the country has exports and domestic market potential that adds nearly 342.8 billion to its GDP annually [1]. Outshone by these economic advantages, the mining industry is an important anthropogenic contributor of particulate atmospheric contamination which are especially released during the extraction, crushing, blasting, and haulage processes [2]. Among these pollutants, PM is notably predominant due to the mechanical disturbances of rock and soil during extraction activities [3]. High PM10 levels impair the quality of ambient air and are dangerous to human health since particles in the air can enter the respiratory tract and cause

   chronic respiratory and cardiovascular conditions [2,3]. The World Health Organization estimates 4.2 million premature deaths annually due to global outdoor air pollution, with industrial and mining host-communities being disproportionately exposed due to their close proximity to emission sources [4]. Analogously, empirical research in Thailand records much higher PM10 levels near mining zones compared to background locations, resulting to corresponding elevated respiratory and cardiovascular health hazards [3,5]. The wider economic impact of particulates in Thailand has been estimated by the International Institute of Applied Systems Analysis and the United Nations Environment Programme as over $18 billion in health damage, which equals about 2.7% of the national GDP [6]. To mitigate these environmental and public health impacts, Thailand regulates mining operations under the Minerals Act B.E. 2560, which requires mining operators to conduct environmental assessments and implement mitigation strategies prior to receiving operational approval and during the entire life-cycle of a mining operation [7]. The objectives of these mitigation strategies are to minimize the amount of particulate emissions produced during mining activities and to restrict the effects of particulate emissions on the environment and human health [7]. Previous research has predominantly been concerned with quantifying the levels of ambient particulate matter at mining locations or quantifying environmental impacts using monitoring data [2,3,5,8,9]. Although these studies are valuable in providing significant indicators of the level of pollution and exposure risk, most of them fail to quantify and model the estimated emissions reductions to comparable economic terms which facilitate operational, investment and policy decisions. Mining activities require adequate economic knowledge of their emission management strategies, to enable prioritization and optimization of investments to obtain optimal compliance and operational outcomes.

   To address this gap, this study applies the emission factor framework from the United States Environmental Protection Agency to estimate annual PM emissions from key mining activities in a limestone quarry. The resulting emission reductions are then translated into financial terms using social cost proxies of PM10 in Thailand, to enable comparison of the economic benefits associated with emission mitigation strategies and enable effective and informed investment decision.

2. METHODOLOGY

   Operational datasets were obtained from 2024 Environmental Impact Assessment (EIA) reports accessed through the EIA database of the Office of Natural Resources and

   Environmental Policy and Planning (https://eia.onep.go.th/eia/detail?id=13736).

   The reports contains information on air-quality mitigation measures, key operational parameters and investments. PM emissions from mining operations were estimated using an activity-based emission inventory approach based on AP-42 emission factors from the United States Environmental Protection Agency [10]. Emissions were calculated for three primary operational sources: blasting, haul-road transportation, and mineral processing (crushing, screening, and material transfer). Activity data from the EIA reportssuch as blasting frequency, vehicle characteristics, road conditions, and annual material throughputwere combined with source-specific emission factors to estimate annual PM emissions, equation (1- 6). Estimates were adjusted to reflect the effectiveness of installed dust-control measures. Total PM emissions were obtained by aggregating emissions across the three operational sources. Emission reductions were determined by comparing uncontrolled baseline emissions with emissions after mitigation controls. The quantity of reduced PM emissions was then monetized using a unit social cost per ton of particulate pollution (/ t¹), enabling the economic valuation of emission reductions achieved through operational mitigation measures, equation (7). Accordingly, PM emissions from blasting were calculated using the scaling factor:

   emission is obtained by multiplying the annual reduction in PM emissions by social cost per ton (CT-1), expressed in Thai Baht ().

   C = ER × CT-1 (7)

3. RESULTS

   Mining operations at the study site generated PM emissions primarily from blasting, haul-road transport, and mineral processing activities. Blsting emissions were estimated using emission factors derived from blast area and adjusted to the PM fraction. Annual uncontrolled PM emissions from blasting were estimated at 5.9 t yr¹, which decreased to 4.4 t yr¹ after applying a control efficiency of 25%, table S1. Control efficiency represents the proportion of emissions reduced by a specific mitigation measure, such as haul-road watering or conveyor belt dust suppression, as defined in the U.S. EPA AP- 42 emission factor guidelines [10]. Haul-road transportation represented the dominant emission source. Using operational parameters including daily production, vehicle payload, haul distance, and trip frequency, uncontrolled PM emissions from transport were estimated at 247.09 t yr¹. Application of haul- road dust suppression measures (control efficiency = 0.55)

   reduced emissions to 111.19 t yr¹, table S2 (supplementary material). Emissions from mineral processing activities

   Then annual emissions from blasting (Eb) are given as:

   where Bf is the blast frequency per year and CE is Control

   Efficiency. Haul transport emission was estimated following the EPA AP-42 method for industrial unpaved road, given as:

   including screening, crushing, and conveyor transferwere estimated using process-specific emission factors from the United States Environmental Protection Agency AP-42 database. Based on an annual production capacity of 2.1 million tons, uncontrolled emissions were estimated at 24.82 t yr¹.

   s W After applying operational dust-control measures, emissions

   Where E is PM emission factor (g/VKT), s is surface material silt content (%), and W is mean vehicle weight (tons). Accordingly, annual PM emissions from haul transport () is given as;

   Where (PM10 ) is the daily emission rate, N is number of operational days, (106) is conversion factor to tons, and CE is Control Efficiency. Similarly, emissions from mineral processing were estimated as;

   decreased to 2.35 t yr¹, corresponding to an approximate 90% reduction, table S3. Aggregating emissions across the three operational sources yielded a total uncontrolled PM emission of 278 t yr¹ for the mine. Implementation of emission control measures reduced total emissions to 117.97 t yr¹, representing an overall reduction of 159.8 t yr¹ (57.6%). Transport activities accounted for the largest share of emissions, indicating that haul-road dust management represents the most significant opportunity for emission reduction, figure 1. Using a PM social cost value of 615,834 t¹ [6], the avoided emissions correspond to an estimated economic benefit of approximately

   98.4 million.

   Where Ep is annual emissions from mineral processing (kg yr¹ or t yr¹), A is amount of material processed annually (tons yr¹), EF is uncontrolled emission factor (kg ton¹) specific to each sub-operation (crushing, screening, or transfer), CE is the Control Efficiency.

   The total PM emission reduction (ER) is calculated as the sum of reductions from blasting, transportation, and processing, which is determined by the difference between uncontrolled (baseline) and controlled emissions, as follows:

   Where the subscripts , , and denote blasting, transportation, and processing, respectively. The economic value of reduced

   Blasting Transport Processing

   Uncontrolled Scenario (t yr¹) Controlled Scenario (t yr¹) Contribution (%)

   100%

   Percentage (%)

4. DISCUSSIONS
   1. Operational drivers of PM emissions

      The emission inventory indicates that PM10 sources are very unevenly distributed in the quarry setting with the haul-road transportation contributing some 89% of the total amount of uncontrolled emissions, mineral processing (9%) and blasting (2%). This trend suggests that mobile operating processes are dominant contributors of particulate emissions than point-source activities, a trend that is quite common in open-pit and quarrying activities where repeated movements of vehicles through unpaved surfaces produce a significant level of dust emissions [11]. The preponderance of haul-road emissions points to the high sensitivity of the particulate emissions to the logistical and operational factors, namely, including haul distance, trip frequency, vehicle payload, and road surface conditions [12]. According to sensitivity analysis, trip frequency (Tf) and haul length (Hl) have a direct positive correlation with the intensity of emissions, implying that when these variables increase, the level of particulate generation also increases proportionately [13]. Conversely, vehicle payload capacity (Pc) exhibits an inverse correlation with emissions since increased payload leads to less trips required to move an equivalent volume of production [12,13]. Therefore, optimizing payload capacity presents both a relevant operational mechanism and investment strategy for lowering dust emissions without compromising production output. Vehicle speed also plays a significant role in determining emission intensity. Scenario analysis shows that increasing haul velocity to 32 km h¹ results in a 6% increase in PM emissions, rising from 111.19 t yr¹ to 117.86 t yr¹ (equation S3-S5). High speeds enhance the kinetic contact between tires and road surfaces that leads to high suspension of fine particles [14]. This shows that controlling haul speeds offers an effective operational strategy for minimizing dust generation, especially when it is complemented by other control measures such as water spraying or surface stabilization, which is in congruence with [14]. Although the proportion of blasting to the overall emissions is relatively low, its output is directly proportional to both the size and frequency of blasts, therefore when the intensity of blasting increases, the localized release of particulate matter may likely increase. Similarly, emissions from crushing and screening operations scale with production throughput because higher material processing rates increase the mechanical disturbance of rock and mineral particles [5,9]. These relationships demonstrate that production intensity remains a fundamental determinant of emission generation across all operational stages.

   2. Effectiveness of emission control strategies

      The implementation of emission control systems in the mine significantly reduced PM emissions across all activities. This is shown in this case-study, where total emissions reduced from

      278 t yr¹ to 117.97 t yr¹ after implementing mitigation measures and this corresponds to a 57.6% emission reduction. Transport emission controls yielded the largest reduction, by decreasing emissions from 247.09 t yr¹ to 111.19 t yr¹, which was achieved primarily through road dust suppression strategies like water spraying and surface maintenance. Mineral processing controls achieved the highest relative reduction, lowering emissions from 24.82 t yr¹ to 2.35 t yr¹, which reflects

      the effectiveness of localized point-source mitigation systems including enclosure structures and dust collectors. These findings therefore indicate that different operational stages often require distinct investments in mitigation strategies to achieve effective emission reduction [3]. Point-source activities such as crushing and screening benefit most from technological controls that capture particulate matter at the emission souce, while mobile sources such as haul trucks require operational and pathway-based interventions, like road surface treatment, buffer systems, speed management, and optimized logistics [15,16]. By integrating both approaches of technological suppression at emission points and operational efficiency in transport logistics, mining operations can create a hybrid mitigation system which is capable of maximizing overall emission reduction.

   3. Implications for operational and policy decision-making

   The apparent interaction between production operations and emission generation implies that emission inventories ought to be incorporated into the production planning, and operational optimization systems. By integrating emission variables to operational information, mines can assess the impact of variations in production scale, investment categories, equipment configuration, or logistics strategies on environmental performance. By translating emission reductions into monetary values mining operations can attain decision-support mechanism for evaluating environmental investments [17, 18]. Furthermore, quantifying the economic benefits of these avoided emissions facilitates evaluation of mitigation costs and their broader environmental returns, thereby supporting more informed investment decisions and strengthening the economic case for proactive environmental management [19,20]. Such analytical frameworks are especially useful in the larger context of mining sector in Thailand, which contributes over 342 billion a year to national GDP, since they can be used to assess the environmental mitigation strategies not only as a means of regulatory compliance but also as a means of sustainable economic growth, better health outcomes in the populations, and environmental stewardship.

5. CONCLUSIONS

This study illustrates that integration of emission-factor-based inventories and economic valuation can reinforce environmental decision-making in mining operations. By using the US-EPA AP-42 methodology, PM emissions from key operational activities were quantified and translated into economic terms. The analysis identifies that logistical operational processes, especially the haul-road transportation, are the most prevalent sources of particulate emissions. These findings indicate that operational changes such as the optimization of the haul logistics, speed and dust suppression systems have a significant potential of reducing the amount of particulate emissions without compromising production efficiency. However, the analysis is constrained by the use of certain standardized emission factors, which may introduce uncertainties when representing site-specific operational dynamics and localized environmental risks. The general implication is that economic translation of environmental performance offers a viable system for facilitating investment decisions, operational efficiency, environmental protection and public health objectives. This form of analytical framework, applied to the resource sector of

Thailand, can eradicate the disparity between environmental compliance and sustainable economic development, so that mining operations can serve as a driver of economic growth at minimum long-term environmental and health damages.

ACKNOWLEDGMENT

The author acknowledges the use of AI tools (QuillBot) for minor grammatical and comprehension adjustments.

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Supplementary materials

Formulars

Percentage Reduction = UncontrolledControlled ×100 (S1)

Uncontrolled

%Reduction = C

Cstandard

×100 (S2)

s (

E = k× S )a (S3)

30

where E is the relative emission factor, S is the vehicle speed (km/h), a is the speed exponent (typically 0.9 for PM, as defined in

the U.S. EPA AP-42 Section 13.2.2), and k is a proportional constant (omitted for relative comparisons). Speed Adjusted PM10 levels (SAPM10) were further examined using;

SAPM10 = BPM10 ×Es (S4)

Where SAPM10 is the speed-adjusted PM levels (tons/day), BPM10 is the Baseline PM (tons/day) and Es is the relative emission factor for speed. To analyze the degree of change under speed consideration, the relation was determined by;

% C =

SA10B10

(

B10

) ×100 (S5)

Tables

Table S1. PM10 emission from Blasting under uncontrolled and controlled scenarios

Table S2. PM10 emissions from transportation under uncontrolled and controlled scenarios

Table S3. PM10 emissions from mineral processing under uncontrolled and controlled scenarios

Table S4. results of the speed sensitivity analysis