Study on the Precise Evaluation of Environmental Impacts of Air Pollution in Cold Regions Using the Cost Control Method

Abstract

Objective: With the acceleration of industrialization, air pollution has become a global environmental issue, particularly in cold regions where the unique climatic and geographical conditions give rise to distinctive types of air pollution and impacts. Considering the economic evaluation of environmental damage is crucial for effective pollution control policies, this study aims to provide a more precise environmental damage assessment method through the Improved Virtual Control Cost Method (IVCCM) to optimize air pollution governance strategies in cold regions. Method: This study utilizes a case study of a major company producing methanol and coal-based natural gas, where the emissions from the boiler exhaust exceeded the prescribed standards for particulate matter, sulfur dioxide, and nitrogen oxides during a specific period. By employing a segmented counting approach that accounts for downtime, precise calculations were conducted for the actual periods of excess emissions. Adjustments were made to the calculation coefficients within the Virtual Control Cost Method to more accurately reflect the ecological damage caused by air pollution. Results: The IVCCM calculations revealed that the total environmental loss caused by the company’s excessive air pollution emissions amounted to USD 1.6844 million, significantly lower than the original calculation method (USD 2.1885 million). Specifically, the environmental losses due to particulate matter, sulfur dioxide, and nitrogen oxides were USD 0.0032 million, USD 0.3600 million, and USD 1.3212 million, respectively. Conclusions: The IVCCM enables a more precise assessment and prediction of ecological environmental damage caused by air pollution in cold regions. Compared to traditional methods, it effectively reduces assessment costs, mitigates disputes arising from unclear parameter values and calculation methods, and facilitates the development of more rational environmental protection policies and measures.

1. Introduction

The acceleration of global industrialization has led to air pollution becoming a global environmental issue [1,2,3]. This problem is particularly severe in cold regions [4]. The unique climate and geographical conditions of cold regions, such as prolonged low temperatures, frequent snow and ice events, and low wind speeds, contribute to the distinct types and impacts of air pollution in these areas [5]. These factors significantly affect the dispersion and deposition of atmospheric pollutants, thereby exacerbating environmental pollution problems. During winter, the increased demand for heating results in a significant rise in emissions of pollutants from activities like burning coal and fuel [6,7]. Furthermore, under cold climate conditions, pollutants tend to linger and accumulate in the atmosphere at a slower rate, exacerbating the degradation of air quality [8]. For instance, the frequent occurrence of haze in Northeast Asia during winter has significantly affected public health and quality of life [9,10]. The specific climate conditions of cold regions also increase the challenges in controlling air pollution [11,12,13]. Hence, addressing air pollution in cold regions is not only a crucial research topic in the field of environmental science but also a significant challenge for public health and socio-economic development [11,12,14].

Existing methods for assessing air pollution damage mainly include market valuation, substitution, and virtual cost methods. While these approaches have theoretical and practical relevance, they often fall short when applied in cold regions. Market valuation relies on market prices of environmental resources; however, the imperfect market structure and pricing system in cold regions result in lower reliability of assessment outcomes. Substitution methods indirectly assess environmental damage by considering the costs of replacing resources or technologies, but limitations in the feasibility and applicability of substitution resources or technologies in cold regions affect the accuracy of the assessments. As a relatively straightforward assessment approach, the Virtual Cost Method quantifies environmental damage by calculating the virtual costs of pollution control. Nevertheless, traditional virtual cost methods often overlook cold regions’ unique climates and geographical conditions in parameter selection and calculation processes, leading to assessment results deviating from reality. Additionally, in evaluating emissions exceeding pollution standards, traditional methods often fail to accurately consider the shutdown time of enterprises, thus affecting the precision of assessments [15]. Therefore, improving and optimizing existing methods are essential for assessing air pollution damage in cold regions.

To enhance the accuracy and reliability of environmental damage assessments for air pollution in cold regions, the Improved Virtual Control Cost Method (IVCCM) is crucial. The enhanced method should consider the climate characteristics of cold regions, such as low temperatures, low wind speeds, and frequent snowfall, and their impact on the diffusion and deposition of pollutants. By incorporating these specific environmental factors, the accuracy of assessments can be improved. Furthermore, the improved method should integrate segmented counting methods to thoroughly examine the actual emission levels of enterprises during specific periods [16,17,18]. Particularly for emissions from industrial boilers, the irregularity of shutdown and startup times is a key factor in accurately calculating the periods of actual excess emissions. These improvements enable a more precise assessment of pollution damage and provide a scientific basis for air pollution control in cold regions, thereby enhancing environmental protection efforts.

Using a case study of a company primarily producing methanol and coal-based natural gas, this paper applies the IVCCM to assess the environmental damage caused by excess particulate matter, sulfur dioxide, and nitrogen oxides emitted from the company’s boiler exhaust during specific periods. Through adjustments of calculation coefficients and considerations of the ecological characteristics of cold regions, the study accurately calculates the losses due to pollutant emissions [19,20,21]. The specific methodologies involve collecting emission data from the company during different periods, employing segmented counting methods, and comprehensively recording and analyzing shutdown and startup times. Subsequently, key parameters in the Virtual Control Cost Method are adjusted based on the climate conditions of cold regions to align with reality. Finally, by calculating the virtual costs of controlling different pollutants, the study provides a comprehensive assessment of the total environmental losses caused by excess emissions from the company [22]. The results indicate that the IVCCM significantly reduces the costs of environmental damage assessments while enhancing the accuracy and reliability of the outcomes.

The primary objective of this study is to present a more precise method for assessing environmental damage from air pollution in cold regions using the IVCCM. The innovation of this study lies in the first application of the Virtual Control Cost Method to quantify point-source atmospheric environmental damage in cold regions. Adjusting calculation coefficients can more accurately reflect cold regions’ unique ecological and environmental damage. This method effectively reduces assessment costs, minimizes disputes arising from ambiguous parameter values, and serves as a scientific basis for formulating environmental protection policies in cold regions [23,24]. Specifically, by adjusting calculation coefficients and including the unique climate conditions of cold regions, the study aims to enhance the accuracy of air pollution damage assessments and provide data support for optimizing environmental governance strategies. Additionally, the results of this study have significant implications for developing more rational pollution control policies. By accurately evaluating the environmental damage caused by pollutant emissions, resources for pollution control can be allocated more scientifically, thereby improving the effectiveness of pollution management. Ultimately, these measures contribute to enhancing the ecological quality of cold regions, elevating the quality of life and health levels of the public, and holding essential scientific and clinical significance [25]. This research hopes to offer new perspectives and methods for air pollution control in cold regions, driving advancements in environmental science and public health.

2. Materials and Methods

2.1. Study Area and Sample Collection Methods

The study area is in Northeast China, where the average winter temperature is below −10 °C, with minimum temperatures reaching as low as −30 °C. The annual snowfall exceeds 100 cm, and the snow depth in winter can reach up to 50 cm. The average wind speed is relatively low, often below 3 m/s in winter, further limiting the dispersion of pollutants. These characteristics result in longer retention times and slower dispersion rates of pollutants in the atmosphere, exacerbating the environmental impact of air pollution. To better illustrate the study area, we have marked the specific location in Figure S1.

The case study company selected for this research is an enterprise primarily producing methanol and coal-based natural gas. The main criteria for selecting this company were its typical representativeness in cold regions and the high types and concentrations of pollutants it emits, making it valuable for study. The data were obtained from the company’s Continuous Emission Monitoring System (CEMS) and manual sampling methods.

The company uses the Continuous Emission Monitoring System (CEMS) to monitor its major pollutant emissions, including sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter (PM). The CEMS system can monitor and record real-time emissions data, ensuring data accuracy and continuity. Additionally, the company conducted manual sampling. The manual sampling method includes collecting emission samples at different intervals and sending them to the laboratory for chemical analysis. This approach can further ensure the accuracy and reliability of the CEMS data. These data include regular reports from environmental monitoring stations and statistical yearbooks published by relevant government departments. The CEMS system, installed at the company’s emission outlets, uses optical, chemical, and physical sensors to ensure data accuracy and continuity. Through the CEMS system, we can obtain hourly emission data, thereby comprehensively assessing the company’s pollution emission levels.

2.2. Economic Impact Assessment Method

To accurately assess the economic impact of industrial emissions when data are not sufficiently precise, we employed the Virtual Control Cost Method (VCCM). This method introduces adjustment coefficients to effectively handle data variability and uncertainty, thereby improving the accuracy of the assessment results. The adjustment coefficients are calibrated based on historical data and related research to compensate for data inaccuracies, including pollutant dispersion, environmental sensitivity, and economic impact coefficients. Although precise measurement data are ideal, VCCM can provide reliable economic impact assessments through robust estimation methods when data are limited or not sufficiently precise. We comprehensively considered the variability of different data sources and historical data to ensure the robustness and reliability of the assessment results. To further enhance the accuracy of the assessment, we conducted rigorous validation and calibration of the data used. By comparing different data sources and adopting cross-validation methods, we ensured the accuracy and reliability of the adjustment coefficients and model parameters.

2.3. Early Virtual Governance Cost Calculation

This method is applicable when the factual existence of pollutant emissions is present, but observations of ecological and environmental damage or emergency monitoring are not timely, the facts of the damage are unclear, or the ecological environment has naturally recovered, or when ecological environmental damage cannot be fully restored through restoration projects; or when the costs of implementing restoration projects far exceed their benefits, the Virtual Control Cost Method is used to calculate the amount of environmental damage [3].

The principle behind this method involves calculating the expenditure required to release pollutants into the environment based on the product of pollutant emissions and the cost of treating one unit of pollutant using current treatment technologies and intensity levels. Subsequently, by multiplying the environmental functions of the area by the corresponding coefficient, the ecological damages caused by air pollution can be quantified. Although the method theoretically specifies conditions and parameter values for its use, its widespread application is limited in practice due to the difficulty in obtaining treatment costs for industrial units and the relatively low estimates of quantified environmental damage [4,5].

2.4. Adjusted Calculation of Virtual Governance Costs

By conducting on-site investigations, verifying data, and reviewing archives, the emission facts of air pollutants are determined, along with identifying their sources or industries, emission patterns, discharge destinations, emission locations, emission amounts, emission concentrations, and functions of discharging into the atmosphere. The suitability of the Virtual Control Cost Method is analyzed. Environmental monitoring, production records, and experimental data are utilized to determine the quantity of air pollutants through field investigations and personnel interviews. The incurred costs of reducing emissions or managing units of air pollutants in industrial enterprises or specialized pollution control companies are quantified using actual investigation methods and cost function methods. By identifying adjustment coefficients based on the sensitivity of pollution points in the emission areas, excess pollutant levels, and types of environmental air functional zones, factors including hazard coefficients, receptor sensitivity coefficients, excess coefficients, and environmental functional coefficients are determined. Taking into account the number of air pollutants, unit governance costs, and adjustment coefficients, the calculation formula of the Virtual Control Cost Method is applied to determine the amount of ecological damage to the atmospheric environment, considering factors such as the hazards of air pollutants, surrounding sensitive points, pollutant excess situations, and the types of environmental functional zones in the affected area.

2.5. Comparison of Two Calculation Methods

Compared to the original calculation method, the new approach eliminates the fixed fee standard and prioritizes quantifying the cost of managing air pollution through actual surveys. To mitigate individual differences among various enterprises, the actual survey method stipulates that a minimum of three companies should be surveyed, with data spanning the past five years. The new method introduces criteria for applying cost function methodology, requiring a sufficiently large sample size from surveys such as pollution source inventories and environmental statistics to establish cost functions for the primary air pollution units in typical industries. Variables within the function should encompass regions, industries, and pollution control processes and incorporate price indices to reflect changes in survey costs compared to current costs. On this basis, specific industry unit operating costs for air pollution control are calculated [6].

Furthermore, the new calculation method integrates considerations of pollutant risks, receptor sensitivity, duration, and environmental functional attributes and specifies adjustment coefficients within a range of 3.5 to 8.4. This specification prevents excessively high control costs caused by the original method and mitigates issues with extremely high coefficients that occur under conditions involving toxic gases or hazardous substances.

As depicted in

Table 1. Comparison of the Range of Adjustment Coefficients between the New and Old Methods.

Original Calculation Method		New Calculation Method
Sensitivity coefficient of environmental functional area	3–5	Hazard coefficient	1–2
		Receptor sensitivity coefficient	1–1.5
It involves toxic and harmful gases	6–20	Superscalar factor	1.1–1.4
		Environmental function coefficient	1.5–2.5

, the new calculation method significantly adjusts various environmental and risk adjustment coefficients compared to the original method. These adjustments reflect the new method’s aim to ensure more accurate calculation results while attempting to avoid inaccuracies in cost estimation resulting from extreme coefficient values seen in previous methods.

2.6. Quantification of Environmental Damage in the Original Calculation Method

2.6.1. Quantitative Representation

$$\mathrm{D}=\sum {\mathrm{G}}_{\mathrm{i}}\times {\mathrm{B}}_{\mathrm{i}}\times {\mathsf{\mu }}_{\mathrm{i}}$$

(1)

Formula: D—Virtual treatment cost of air pollutants, in USD; G—Total amount of excess category I air pollutants, in tons; B—Unit treatment cost of category I air pollutants, in USD/ton; μ—Adjustment coefficient for category i.

2.6.2. Calculation Formula for Total Air Pollutant Emissions

Formula: G₁ represents the total amount of air pollutants emitted over time t that exceed the limit for Type i pollutants; I denote the chimney stack; j stands for the assessment period; G_1ij signifies the excess emission of Type i pollutants from chimney stack j daily; R_1ij indicates the concentration of air pollutants measured daily from chimney stack j in milligrams per cubic meter (mg/m³); V_1ij represents the daily flow rate of air pollutant emissions from chimney stack j in cubic meters per day (m³/d); and B_1i is the concentration limit for air pollutant emissions in milligrams per cubic meter (mg/m³).

$${\mathrm{G}}_1=\sum {\mathrm{G}}_{1\mathrm{i}\mathrm{j}}$$

(2)

$${\mathrm{G}}_{1\mathrm{i}\mathrm{j}}={\mathrm{V}}_{1\mathrm{i}\mathrm{j}}\times \left({\mathrm{R}}_{1\mathrm{i}\mathrm{j}}-{\mathrm{B}}_{1\mathrm{i}}\right)\times {10}^{-9}$$

(3)

2.6.3. Determining Unit Governance Costs

Formula B represents the unit air pollution treatment cost in USD per ton, M is the operational cost in USD, Q_a is the number of air pollutants generated in tons, and Q_c is the number of air pollutants emitted in tons.

$$\mathrm{B}=\mathrm{M}/({\mathrm{Q}}_{\mathrm{a}}-{\mathrm{Q}}_{\mathrm{c}})$$

(4)

2.7. Quantifying the Value of Environmental Damages Using New Computational Methods

2.7.1. Quantification Formula

The formula for quantification is as follows: D represents the cost of air pollution and ecological degradation in monetary units; E stands for the quantity of air pollutants in metric tons; C denotes the cost of processing one ton of air pollution in monetary units; γ signifies the adjustment coefficient; α refers to the hazard coefficient; β represents the sensitivity coefficient of the receptors; ω symbolizes the environmental function coefficient; and τ corresponds to the factor for exceeding limits.

$$\mathrm{D}=\mathrm{E}\times \mathrm{C}\times \mathsf{\gamma }$$

(5)

$$\mathsf{\gamma }=\left(\mathsf{\alpha }\times \mathsf{\beta }+\mathsf{\omega }\right)\times \mathsf{\tau }$$

(6)

2.7.2. Verification of Air Pollutant Quantities

• Principles for Determining Air Pollutant Quantities

The amount of air pollutants is determined based on emissions approved by the ecological environment management department. The excess emission quantity is calculated if emissions exceed the standard at the approved emission points and are released into the atmosphere. The discharge amount is considered as the total gas emissions for other instances of illegal discharges.

In cases where essential parameters are missing in monitoring data, relevant materials such as pollution emission records in current penalty documents, environmental impact assessment reports, discharge permit reports, feasibility study reports, interrogation records, and case files can be analyzed and verified.

If the pollution distribution is consistent in scenarios involving both excess emissions and total emissions, the calculation results for the number of air pollutants for each scenario are taken from the respective calculations for the number of air pollutants.

2.

Measurement of Concentration Method

The actual concentration method calculates the quantity of pollutants based on the monitoring data of air pollution. It is mainly applicable for verifying the quantity of air pollutants from fixed pollution sources. The monitoring data of air pollutants include data from continuous online monitoring systems, supervisory monitoring data provided by the ecological department, and product quality inspection data provided by municipal supervisory departments.

When calculating the quantity of pollutants emitted exceeding the standard, the quantity of pollutants is calculated based on the data from the continuous online monitoring system of air pollutants. The calculation formula is as follows:

$$\mathrm{E}={\sum }_{\mathrm{T}}{\mathrm{R}}_{\mathrm{T}}\times {\mathrm{V}}_{\mathrm{T}}\times {\mathsf{\theta }}_{\mathrm{T}}\times {10}^{-9}$$

(7)

$${\mathsf{\theta }}_{\mathrm{T}}={\displaystyle \frac{{\mathrm{Z}}_{\mathrm{t}}-\mathrm{B}}{{\mathrm{Z}}_{\mathrm{t}}}}$$

(8)

The calculation formula is as follows: R_T represents the concentration of air pollutants measured per hour in mg/m³; V_T stands for the hourly exhaust gas flow rate in m³/h; θ_T denotes the proportion of air pollutants emitted above the standard per hour; T indicates the evaluation time in hours; Z_T signifies the hourly decrease in air pollutant concentration in milligrams per cubic meter; B denotes the standard emission concentration limit in mg/m³, using 0 for air pollutants without emission standards; for the meanings of other symbols, refer to Equation (1).

The quantity of air pollutants is calculated based on the supervisory monitoring data of air pollutants. The calculation formula is as follows:

$$\mathrm{E}=\overline{\mathrm{R}}\times \overline{\mathrm{V}}\times \overline{\mathsf{\theta }}\times \mathrm{T}\times {10}^{-9}$$

(9)

$$\overline{\mathsf{\theta }}={\displaystyle \frac{\overline{\mathrm{Z}}-\mathrm{B}}{\overline{\mathrm{Z}}}}$$

(10)

Calculation formula: Average concentration of $\overline{R}$—Air pollutants, in mg/m³; $\overline{V}$—Average exhaust emission flow rate, in m³/h; θ—Average proportion of air pollutants exceeding the emission standard; Z—Average reduction concentration of air pollutants, in milligrams per cubic meter. For other symbols’ meanings, refer to Equations (1) and (4).

When calculating the quantity of air pollutants emitted more than the standards, the number of air pollutants is calculated based on data from the continuous online monitoring system for air pollutants. The calculation formula is as follows:

$$\mathrm{E}={\sum }_{\mathrm{T}}{\mathrm{R}}_{\mathrm{T}}\times {\mathrm{V}}_{\mathrm{T}}\times {\mathsf{\theta }}_{\mathrm{T}}\times {10}^{-9}-{\mathrm{E}}_{\mathrm{a}}$$

(11)

Formula: E_a represents the amount of air pollution emissions permitted according to emission permits; for the meanings of other symbols, refer to Equations (5) and (7). The formula for calculating the quantity of air pollutants based on air pollution monitoring data is as follows:

$$\mathrm{E}={\sum }_{\mathrm{T}}{\mathrm{R}}_{\mathrm{T}}\times {\mathrm{V}}_{\mathrm{T}}\times {\mathsf{\theta }}_{\mathrm{T}}\times {10}^{-9}-{\mathrm{E}}_{\mathrm{a}}$$

(12)

3.

Material Balance Algorithm

The material balance algorithm calculates large quantities based on the law of mass conservation and the varying relationships between raw materials, products, and air pollutants. It is primarily applicable for verifying excessive emissions of air pollutants from fixed pollution sources. The calculation formula is as follows:

$$\mathrm{E}=\mathrm{A}\times \mathrm{K}\times \left(1-\mathsf{\eta }\right)-{\mathrm{E}}_{\mathrm{a}}$$

(13)

Formula: A represents the activity level, where the consumption of raw materials or production of products is selected accordingly in units of t; K stands for the coefficient of air pollution generation; η denotes the removal efficiency of air pollutants by the treatment technology. The meanings of other symbols are specified in Equations (5) and (11).

Values for K and η can be referenced from the national pollution source census results and the peer-reviewed coefficients for production and pollutant emissions accounting. These values cannot be obtained through direct investigation.

2.8. Determination of Unit Governance Costs

2.8.1. Empirical Investigation Method

Conducting empirical research entails identifying companies in the same or neighboring regions with similar production scales, production processes, product types, and treatment processes, handling the same or similar air pollution substances to achieve stable compliance with emission standards for unit pollution control costs. Alternatively, forecasting the costs of pollution treatment schemes that meet the criteria above. Among these factors, achieving stable compliance with emission standards, given similar product types and scales, is the primary consideration, followed by geographical proximity, with production processes and treatment methods being of secondary concern. The calculation method for unit air pollution control costs is as follows:

$${\mathrm{C}}_{\mathrm{i}}={\displaystyle \frac{{\sum }_{\mathrm{n}}{\mathrm{C}}_{\mathrm{i},\mathrm{j}}}{\mathrm{n}}}$$

(14)

$${\mathrm{C}}_{\mathrm{i},\mathrm{j}}={\displaystyle \frac{\mathsf{\lambda }\times \mathrm{F}\times \mathsf{\mu }+\mathrm{c}\left(\mathrm{t}\right)}{{\mathrm{P}}_{\mathrm{i}}\left(\mathrm{t}\right)-{\mathrm{E}}_{\mathrm{i}}\left(\mathrm{t}\right)}}$$

(15)

Computational Formula: C_i represents the unit treatment cost of air pollutant i in USD per ton; n denotes the number of surveyed enterprises, ideally no less than 3; C_i,j signifies the unit treatment cost of air pollutant i for surveyed enterprise j in USD per ton; λ stands for the Price Index, a measure reflecting changes in price levels obtained from national or regional statistical yearbooks; F denotes the fixed cost input for the survey, such as purchasing pollution control equipment in USD. The depreciation factor reflects the wear and tear of pollution control equipment during the control period; c represents the operating cost of air pollution control facilities in surveyed enterprises in ten thousand USD; t denotes the operation time of the air pollution control facilities; P_i is the amount of air pollutant i generated by surveyed enterprise t; E_i stands for the emission situation of air pollution substances by surveyed enterprise i in tons.

2.8.2. Cost Function Approach

Based on actual surveys with a sufficiently large sample size or utilizing databases such as pollution source surveys and environmental statistics, it is possible to establish cost functions for the primary air pollutants in typical industries and subsequently calculate the unit control costs for specific air pollutants in these industries. The calculation formula is as follows:

$${\mathrm{C}}_{\mathrm{i}}=\mathsf{\lambda }\times {\mathrm{f}}_{\mathrm{j}}\left(\mathrm{l},\mathrm{d},\mathrm{k},\mathrm{s}\right)$$

(16)

In the formula, f_j(l, d, k, s) represents the unit cost function of air pollution item j, with l, d, k, and s denoting the region, industry, processing technology, and business scale, respectively. For the meaning of other symbols, please refer to the symbol explanation in Formula (15).

2.9. Data Collection and Processing

The case study company in this research primarily produces methanol and coal-based natural gas. The data used in this study mainly come from government statistical data and environmental monitoring reports. These data include regular reports from local environmental monitoring stations and statistical yearbooks published by relevant government departments. The data cover the study area both temporally and spatially, reflecting the pollution status within the study area. Although these data are not directly measured, they are from reliable sources and are highly representative and accurate.

Government Statistical Data: These data include annual environmental quality reports and statistical yearbooks published by environmental protection departments. They cover information on air pollutant concentrations, emission source distributions, and emission quantities within the study area. These data are published by authoritative institutions, ensuring their reliability and scientific validity.

Environmental Monitoring Reports: The regular monitoring data from local environmental monitoring stations are used and obtained through CEMS and manual sampling methods. These data include concentrations and trends of major pollutants during the study period. The monitoring reports provide detailed time-series data that reflect changes in pollutant concentrations.

3. Results

3.1. Case Introduction

A certain company specializes in producing methanol and coal-based natural gas as its main products, with a designed annual production capacity of 390,000 tons of industrial methanol, 100 million cubic meters of natural gas, and over 70 million cubic meters of gas supply in cold regions. The company’s recent population concentration area lies to the southwest, about 150 m from the coal selection plant’s staff quarters, falling under the second-class environmental functional area designation. During the period from 1 January 2015, to 31 May 2017, the company’s boiler exhaust emissions exceeded the relevant emission standards for air pollutants, with concentrations of particulate matter, sulfur dioxide, and nitrogen oxides surpassing the limits: 30 mg/m³ for particulate matter, 200 mg/m³ for sulfur dioxide, and 200 mg/m³ for nitrogen oxides [7].

Based on the investigation findings confirming the company’s excessive emissions of particulate matter, sulfur dioxide, and nitrogen oxides, a segmented counting method was employed to deduct the shutdown time and calculate the actual periods of excess emissions.

The data presented in

Table 2. Statistics of Particulate Matter Emission Exceedances from 2015 to 2017.

Year	Exceeding Days (Days)	Monitoring Concentration (mg/m3)
2015	13	34.2
	14	35.8
	20	47.3
	13	57.3
	207	65
	14	65.3
	310	68.1
	25	71.3
	12	71.6
	31	71.9
	55	72.3
	54	73.3
	60	77.9
	32	79.6
	25	80
	44	81.7
	32	84.7
	30	84.8
	205	85.8
	93	86.9
	62	89.5
	31	90
	148	95.4
	58	98.9
	148	105.8
	127	129.2
	127	143.9
2016	55	36.1
	38	41.7
	56	42.5
	38	44.8
	96	45.6
	38	46.3
	24	47.3
	34	49
	39	50.2
	39	50.6
	40	50.9
	35	51.9
	39	52.5
	96	53
	24	53.1
	34	54.5
	61	54.8
	96	55.3
	63	55.6
	75	55.8
	24	55.9
	61	56
	34	56.3
	61	57.3
	56	57.8
2017	90	46.1
	49	51.2
	24	52.8
	50	54.5
	38	54.8
	49	55.3
	50	56.4
	24	56.8
	9	57
	49	57.2
	50	58.3

illustrate the variations in particulate matter emissions by the chemical company from 2015 to 2017 (Figure 1). The data indicate that in 2015, there were several months with extremely high concentrations of particulate matter, especially towards the end of the year, where the concentration exceeded 80 mg/m³ multiple times, far beyond the stipulated 30 mg/m³ standard. In 2016, although the overall particulate matter concentration decreased compared to 2015, several months later, the standard limit was still exceeded. As of 2017, despite a reduction in particulate matter concentration, instances of exceeding the limit persisted, indicating that while the company has improved in controlling particulate matter emissions, the effectiveness remains suboptimal.

According to the data presented in

Table 3. Sulfur Dioxide Emission Exceedances Data from 2015 to 2017.

Year	Exceeding Days (Days)	Monitoring Concentration (mg/m3)
2015	13	34.2
	14	35.8
	20	47.3
	13	57.3
	207	65
	14	65.3
	310	68.1
	25	71.3
	12	71.6
	31	71.9
	55	72.3
	54	73.3
	60	77.9
	32	79.6
	25	80
	44	81.7
	32	84.7
	30	84.8
	205	85.8
	93	86.9
	62	89.5
	31	90
	148	95.4
	58	98.9
	148	105.8
	127	129.2
	127	143.9
2016	55	36.1
	38	41.7
	56	42.5
	38	44.8
	96	45.6
	38	46.3
	24	47.3
	34	49
	39	50.2
	39	50.6
	40	50.9
	35	51.9
	39	52.5
	96	53
	24	53.1
	34	54.5
	61	54.8
	96	55.3
	63	55.6
	75	55.8
	24	55.9
	61	56
	34	56.3
	61	57.3
	56	57.8
2017	90	46.1
	49	51.2
	24	52.8
	50	54.5
	38	54.8
	49	55.3
	50	56.4
	24	56.8
	9	57
	49	57.2
	50	58.3

, the company’s sulfur dioxide emissions during the same period are depicted in Figure 2. These data indicate that in 2015, the peak concentration of sulfur dioxide reached 143.9 mg/m³, significantly exceeding the emission standard of 200 mg/m³. Although there was a slight decrease in sulfur dioxide concentration in 2016 and 2017, multiple months still recorded levels surpassing the standard, suggesting that the company’s efforts in controlling sulfur dioxide emissions, while existent, have been insufficient, leading to ongoing environmental risks.

Utilizing the data from

Table 4. Nitrogen Oxides Emission Exceedances Overview from 2015 to 2017.

Year	Exceeding Days (Days)	Monitoring Concentration (mg/m3)
2015	127	236.6
	127	263
	25	314
	310	315
	60	323
	62	342.7
	62	350.7
	25	352
	14	379.7
	54	434
	207	452
	31	497.3
	30	528
	148	531.3
	31	534.3
	14	548.7
	13	575.3
	31	575.7
	55	578
	148	579.3
	32	593.3
2016	39	220
	39	227
	96	235
	70	238
	39	245
	130	248
	34	255
	70	256
	41	270
	34	277
	39	300
	61	302
	24	316
	40	319
	35	337
	35	346
	38	354
	70	355
	56	356
	24	362
	24	366
	56	372
	34	375
	56	393
2017	49	206
	49	213
	49	222
	90	261
	50	294
	50	301
	50	320

, the company’s emissions of nitrogen oxides are illustrated in Figure 3. In 2015, nitrogen oxide concentrations were alarmingly high for several months, particularly towards the end of the year, with levels peaking at 579.3 mg/m³, far exceeding the 200 mg/m³ limit. Despite some decline after that, non-compliance with the specified standards persisted in 2016 and 2017. It indicates that the measures taken to control nitrogen oxide emissions have not been effective enough, necessitating the company’s implementation of stricter control technologies and methods to safeguard environmental security.

The in-depth analysis of these charts reveals the issues in the chemical company’s emissions of particulate matter, sulfur dioxide, and nitrogen oxides. Despite some improvements, the emissions exceed the permissible limits, posing a serious environmental threat. Therefore, there is a pressing need to strengthen environmental protection measures, enhance pollutant treatment efficiency, and ensure compliance with national environmental standards to safeguard public health and safety.

3.2. Estimation of Environmental Losses Due to Excessive Air Pollution Emissions Based on Operating Costs and Emission Volumes

It is crucial to accurately assess the economic impact of industrial pollutant emissions in the field of environmental protection. This section quantifies the environmental damage caused by excess atmospheric emissions in the thermal power industry using actual operating costs and emission volumes based on data from the “2015 Heilongjiang Environmental Statistical Yearbook”, providing a scientific basis for policy-making and optimizing environmental protection measures.

According to the thermal power industry data in the “2015 Heilongjiang Environmental Statistical Yearbook,” we calculated the operating costs and corresponding pollutant emission volumes for bag dust removal facilities, desulfurization facilities, and denitrification facilities. On this basis, the unit cost of dust treatment was found to be USD 3.57, the unit cost of sulfur dioxide treatment was USD 333.14, and the unit cost of nitrogen oxide treatment was USD 468.14. Considering that the evaluated area is classified as a Class II environmental functional area, the reason for this classification is that the region includes both industrial activities and residential areas, fitting the criteria for a mixed-use area. The sensitivity factor is chosen as 3 based on three considerations: high population density, fragile ecological environment, and limited environmental carrying capacity, to ensure the accuracy and scientific validity of the assessment. Using these parameters, we estimated that the environmental damage caused by excess atmospheric pollutant emissions from this company amounted to a total of USD 3,126,429, including USD 4043 for dust damage, USD 512,371 for sulfur dioxide damage, and USD 1,672,086 for nitrogen oxide damage.

In summary, by combining actual operating costs and detailed pollutant emission data, we can accurately quantify and assess the economic and environmental damage caused by excess atmospheric pollutant emissions. This result highlights the urgency of taking effective pollution control measures and provides an important basis for relevant departments to formulate more scientific, environmental policies and management strategies.

3.3. Estimating the Environmental Economic Losses of Excessive Air Pollution Emissions Using a Novel Quantitative Method

To accurately assess the economic impact of industrial emissions on the environment, this study employed a novel quantitative method that meticulously set and calculated the hazard coefficient, receptor sensitivity coefficient, excess coefficient, and environmental functional coefficient in conjunction with the national standard GB30000 series. This method not only enhances the precision of the assessment but also provides a scientific basis for formulating more rational environmental protection policies.

Initially, based on the regulations stipulated in GB30000.18 to GB30000.27, this study classified the categories of harmful pollutants and their mixtures, assigning a hazard coefficient range of 1 to 1.75 for different hazard types. For specific air pollution substances, the hazard coefficient range was determined to be between 1.25 and 2. In cases where the same pollutant belonged to multiple hazard categories, the highest hazard coefficient value was selected. The receptor sensitivity coefficient was determined based on the nearest distance between the air pollution source and densely populated areas in the downwind direction, with distances categorized as 1 km and 5 km, and the sensitivity coefficient range set at 1 to 1.5. Moreover, the excess coefficient was established based on the multiple emission concentrations exceeding national or local industry emission standards and comprehensive emission standards, with the range set at 1.1 to 1.4. The environmental functional coefficient was then set according to the environmental functional zone category where the pollution source was located, with coefficients of 2.5 designated for areas like natural reserves and scenic spots and coefficients of 1.5 for residential, commercial, cultural, industrial, and rural areas.

During the specific calculation process, data from the “2015 Heilongjiang Environmental Statistical Yearbook” were used to determine the unit treatment costs for SO₂, NO_x, and PM, which were USD 3.57, USD 333.14, and USD 468.14, respectively. Using these data and the above parameters, it was calculated that the total environmental damage caused by the company’s excess atmospheric pollutant emissions amounted to USD 1.6844 million, including USD 0.3157 million for dust damage, USD 36.0000 million for sulfur dioxide damage, and USD 1.3212 million for nitrogen oxides damage.

In conclusion, this study meticulously estimated the environmental economic losses associated with excessive air pollution emissions by adopting the new quantitative method in conjunction with national standards. These findings not only validate the new method’s effectiveness but also underscore the importance of continuous optimization of pollution control technologies and strategies to mitigate the adverse environmental impacts of industrial activities.

3.4. Comparative Analysis of Environmental Loss Costs between New and Old Virtual Governance Cost Methods

We examined the impact of different calculation frameworks on estimating the environmental loss costs of excessive emissions of dust, sulfur dioxide, and nitrogen oxides (Figure 4).

According to the research results, the total environmental damage calculated using the original VCCM amounted to USD 2.1885 million, with losses of USD 0.004 million for dust, USD 0.5124 million for sulfur dioxide, and USD 1.6721 million for nitrogen oxides. Using the new VCCM, the total environmental damage cost decreased to USD 1.6844 million, with specific losses of USD 0.0320 million for dust, USD 0.3600 million for sulfur dioxide, and USD 1.3212 million for nitrogen oxides. This comparison clearly shows that the new calculation method results in lower environmental damage costs for all types of pollutants, with reductions of USD 0.0080 million for dust, USD 0.1524 million for sulfur dioxide, and USD 0.3509 million for nitrogen oxides, totaling a reduction of USD 0.5041 million.

Overall, the new virtual governance cost calculation method is relatively more conservative than the original method when estimating the environmental loss costs due to air pollution, reflecting lower estimated environmental losses. This difference underscores the significant role of calculation methods in environmental policy-making and resource allocation, providing a strong basis for further optimizing the calculation of environmental loss costs.

4. Discussion

This paper uses a company primarily producing methanol and coal-based natural gas as a case study. The study focuses on the excess emissions of SO₂, NO_x, and PM from the company’s boiler exhaust during specific periods, applying the IVCCM for environmental damage assessment. The results of the original VCCM showed that the total environmental damage caused by the company’s excess emissions amounted to USD 2.1885 million. After applying the adjusted method, the estimated damage was reduced to USD 1.6844 million, significantly lowering the total assessment. Specifically, the environmental damages for dust, sulfur dioxide, and nitrogen oxides were USD 0.0032 million, USD 0.3600 million, and USD 1.3212 million, respectively. The adjusted VCCM reflects a more accurate calculation, representing a more realistic environmental damage scenario.

This study provides a more precise method for assessing the environmental damage caused by atmospheric pollution in cold regions by IVCCM. The results have significant implications for the formulation of environmental governance strategies. Firstly, the standardized calculation method offered by this study helps enhance the scientific and transparent nature of environmental governance decisions. Policy-makers can use this study’s data and assessment methods to quickly and accurately assess the environmental damage of industrial emissions, thereby formulating more scientific and effective pollution control measures. Secondly, by quantifying the economic losses caused by excess emissions, this study highlights the urgency of taking effective pollution control measures. Specific economic loss data can help governments and enterprises better understand the economic impact of environmental pollution, thereby paying more attention to environmental protection and investing more resources in pollution control and environmental governance. Finally, the methods and results of this study can provide references for environmental governance in similar cold regions. This method can be validated and improved by applying it to different regions and industries, increasing their universality and application value and providing a scientific basis for global environmental governance in cold regions.

By IVCCM, we can assess environmental damage more accurately and better reflect the economic impact of air pollution on public health. Specifically, we introduced adjustment coefficients in the IVCCM that consider the long-term impact of air pollution on human health. For example, pollutant dispersion, environmental sensitivity, and economic impact coefficients all consider public health impacts. With these adjustment coefficients, we can more accurately estimate the economic costs of health problems such as respiratory and cardiovascular diseases caused by pollutant emissions. This method can be used to assess pollution damage from different types of enterprises and provide a scientific basis for air pollution control in cold regions. For example, the emission characteristics and environmental impacts of different industrial enterprises vary under different climatic conditions, and applying the improved method can more accurately assess their environmental damage, leading to more effective control measures [26]. Moreover, this method plays a crucial role in policy-making, providing reliable assessment data for governments and relevant departments to support more scientifically reasonable environmental protection policies [27,28,29].

The innovation of this study lies in the first application of the IVCCM to quantify point-source atmospheric environmental damage in cold regions. Traditional VCCM often overlooks cold regions’ unique climatic and geographical conditions when assessing air pollution damage, leading to results that deviate from actual conditions. This study improves the VCCM by incorporating specific climatic factors of cold regions, such as low temperatures, low wind speeds, and frequent snowfall, making it more realistic. Additionally, the study employs the segmented counting method to account for actual emissions during specific periods and accurately calculates boiler startup and shutdown times. These improvements significantly enhance the accuracy and reliability of the assessment.

Compared to other methods, the IVCCM can more accurately quantify the environmental damage caused by pollutant emissions, providing a more scientific basis for environmental protection. The study results show that using the improved method, the cost of pollution damage assessment in cold regions is significantly reduced, and the assessment accuracy is markedly improved. This innovative method is significant for ecological environment management and pollution control policy-making in cold regions and provides a reference for other similar regions. By accurately assessing the environmental damage caused by pollutant emissions, resources for pollution control can be allocated more scientifically, improving the effectiveness of pollution control and ultimately enhancing the ecological environment quality and public health in cold regions.

Although this study has made certain achievements in method improvement and application, there are still some limitations. Firstly, the limitations of data sources may affect the generalizability of the assessment results; secondly, the applicability of the improved method is mainly concentrated in cold regions, and further verification is needed for applications in other climatic conditions. Additionally, we currently focus on only one company. Future research needs to collect data on a larger scale to further improve and expand the methods to enhance their applicability and generalizability [30,31]. Moreover, continuous research and innovation are crucial for advancing environmental science and public health [32,33]. By continuously improving assessment methods and optimizing pollution control strategies, we can more effectively address global air pollution issues, protecting the ecological environment and public health [34].

5. Conclusions

This study introduces the IVCCM, which provides a precise evaluation model and describes specific methods for calculating and selecting parameters, such as hazard coefficient, receptor sensitivity coefficient, excess coefficient, and environmental function coefficient. These adjustments reduce point-source air pollution control costs, more accurately reflect the ecological damage of the atmosphere, and prevent overestimating costs. The enhanced calculation method considers pollutant risk, receptor sensitivity, duration, and environmental function attributes, leading to more objective and scientific assessment results (Figure 5).

Although this study has made methodological innovations by providing more detailed parameter settings and adjustment methods, some limitations remain. Certain factors in the new calculation method, such as the excess factor k related to pollutant types, are not comprehensively covered in national or regional industry standards, which often results in inaccurate calculations of the excess factor k, influencing the accuracy and applicability of the assessment. Additionally, the new method lacks clarity in identifying methods and sequences when dealing with diverse, complex, or highly toxic air pollution species.

Future research should broaden the model’s applicability by including more types and scales of industrial enterprises and refining pollutant identification methods to enhance their universality and accuracy. Furthermore, with the ongoing deepening of climate change mitigation measures, identifying the environmental damage of non-point-source air pollution becomes an urgent issue. Future work should resolve controversies surrounding these calculation methods and provide more comprehensive theoretical and practical support for quantifying the ecological damage value caused by air pollution.