To better depict the evaluation paradigm and reveal the superiority of these methods, the assessment results are unified and discussed in the below.
4.1 Discussion on life cycle analysis method
Data collection is a key procedure in LCA, where plenty of data needs to be collected, such as energy inputs, raw material inputs, atmospheric emission factors and so on. To ensure the accuracy of this assessment, two types of data are collected. For the case studies of coal-fired power generation and coal liquefaction, their energy efficiency and emission factors are mainly collected from the report issued by the authority and the related mature researches. For the basic data of coal mining, its energy consumption and emission data are chiefly collected from the investigated coal mine. Detailedly, a total of 582 dust samples were collected in the coal mine along with the division of four workplaces, that is, coal face (140 samples), heading face (168 samples), shotcrete point (124 samples), and transshipment point (150 samples). The dust concentrations in these various workplaces are shown in Table 4. Meanwhile, the mass of pollutants emitted from four substages in coal-fired power generation are illustrated as an example, as shown in Table 5. As for coal liquefaction, its related evaluation parameters were collected as well, as illustrated in Table 6.
In brief, the application of LCA could help to ascertain the quantitative polluting condition in different production processing, and thus clarify the worksites and occupants which need to be protected in priority. Such examples might be given easily, as shown in Table 4, the concentrations of dust in the four workplaces during coal mining can be defined, with the range of 3.34–16.85, 2.24–20.28, 1.29–17.38, and 3.69–12.53 mg/m3 for coal face, driving face, shotcreting point, and transshipment point, respectively. It could be found that the concentrations of coal dust are all higher than the occupational exposure limit in each workplace, namely, 4 mg/m3 (Ministry of Health, 2019). As for silica dust, the most serious contamination occurred at driving face with the concentration of 20.28 ± 3.17 mg/m3, which is 20.28 times the occupational exposure limit for silica dust in the workplace (1 mg/m3). Therefore, it is suggested that during coal mining, attentions of pollutant control should be centered on coal dust more than silica dust. Given the prevention and control technologies differ from various pollutants, it would be more cost-saving and effective with shooting the arrow at the target.
Table 4 Dust concentrations and health risks in various workplaces for coal mining.
Workplaces
|
Work types
|
Dust
|
Number of samples
|
Concentrations
(mg/m3)
|
Coal face
|
Shearer operator
|
Coal dust
|
24
|
16.85±4.19
|
Silica dust
|
16
|
3.38±1.02
|
Hydraulic pump worker
|
Coal dust
|
13
|
9.67±1.46
|
Silica dust
|
10
|
4.93±1.34
|
Support worker
|
Coal dust
|
21
|
14.39±2.36
|
Silica dust
|
7
|
4.54±0.71
|
Coal digger
|
Coal dust
|
17
|
16.25±3.47
|
Silica dust
|
12
|
5.89±0.62
|
Scraper conveyor driver
|
Coal dust
|
9
|
10.28±0.24
|
Silica dust
|
11
|
3.34±0.16
|
Driving face
|
Roadheader driver
|
Coal dust
|
12
|
3.97±0.27
|
Silica dust
|
26
|
14.36±1.24
|
Driller
|
Coal dust
|
10
|
4.62±1.25
|
Silica dust
|
19
|
18.95±2.16
|
Muck loader driver
|
Coal dust
|
11
|
2.24±0.38
|
Silica dust
|
23
|
16.54±0.71
|
Belt driver
|
Coal dust
|
14
|
2.73±0.47
|
Silica dust
|
18
|
14.31±0.62
|
Blaster
|
Coal dust
|
11
|
3.62±1.08
|
Silica dust
|
24
|
20.28±3.17
|
Shotcreting point
|
Gunite worker
|
Silica dust
|
11
|
4.92±0.45
|
Cement dust
|
14
|
17.38±0.14
|
Drilling machine operator
|
Silica dust
|
8
|
10.09±1.35
|
Cement dust
|
14
|
2.91±0.19
|
Mixer and feeder driver
|
Silica dust
|
10
|
1.29±1.67
|
Cement dust
|
15
|
16.56±1.33
|
Loading and unloading workers
|
Silica dust
|
12
|
2.72±0.12
|
Cement dust
|
17
|
14.41±0.73
|
Support worker
|
Silica dust
|
9
|
7.83±1.15
|
Cement dust
|
14
|
12.64±1.03
|
Transshipment point
|
Scraper conveyor driver
|
Coal dust
|
9
|
8.07±1.13
|
Silica dust
|
12
|
7.32±0.24
|
Cage driver
|
Coal dust
|
15
|
5.61±3.24
|
Silica dust
|
21
|
9.03±1.14
|
Belt driver
|
Coal dust
|
15
|
4.26±2.35
|
Silica dust
|
9
|
4.52±1.76
|
Transfer conveyor driver
|
Coal dust
|
8
|
10.72±1.49
|
Silica dust
|
13
|
6.34±1.65
|
Repairman
|
Coal dust
|
8
|
8.78±0.85
|
Silica dust
|
16
|
3.69±1.06
|
Coal caving worker
|
Coal dust
|
10
|
12.53±2.41
|
Silica dust
|
14
|
5.31±0.81
|
Table 5 The total mass of airborne pollutants emitted from coal-fired power generation (unit: kg).
|
Substage classification
|
Coal mining
|
Coal transportation
|
Coal combustion
|
Slag disposal
|
Pollutant types
|
CO2
|
2.05E+08
|
4.47E+07
|
9.89E+09
|
5.16E+06
|
CH4
|
4.36E+06
|
3.34E+03
|
6.68E+04
|
3.85E+02
|
N2O
|
3.58E+02
|
1.20E+03
|
2.66E+04
|
1.38E+02
|
CO
|
9.94E+05
|
7.43E+04
|
1.12E+06
|
8.57E+03
|
PM10
|
4.18E+05
|
7.29E+04
|
7.84E+05
|
3.62E+04
|
NO2
|
2.80E+05
|
4.98E+05
|
5.44E+06
|
5.75E+04
|
SO2
|
7.29E+05
|
7.29E+05
|
2.41E+06
|
3.21E+03
|
Table 6 Evaluation parameters for the life cycle of coal liquefaction.
Parameter
|
Value
|
Unit
|
Source
|
Consumption
|
Coal mining and processing
|
Comprehensive energy
|
30.2
|
kgce/t
|
Wang, 2014
|
Electricity
|
25.8
|
kWh/t
|
Coal transportation
|
Energy intensity
|
240
|
kJ/(t∙km)
|
Zhou et al., 2017
|
Fuel mix and percentage
|
Diesel
|
55%
|
--
|
Electricity
|
45%
|
Transport distance
|
72.30
|
km
|
Production data
|
Coal liquefaction
|
Raw coal
|
2.89
|
tce/t
|
Du, 2016
|
Water
|
5.84
|
t/t
|
Production data
|
Electricity
|
692.80
|
kWh/t
|
Production
|
Main product
|
Diesel
|
0.66
|
t/t
|
Production data
|
Liquefied gas
|
0.095
|
Byproduct
|
Naphtha
|
0.23
|
Phenol
|
0.0033
|
Population of the project sites per unit area
|
24
|
people/km2
|
BSOC, 2018
|
Gross area of the city where project sites locate
|
86752
|
km2
|
Key parameters for the calculation of emissions
|
Coal mining and processing
|
SO2
|
Direct emission
|
1200
|
kg/t
|
Production data
|
Indirect emission
|
740.4
|
NO2
|
Direct emission
|
1665
|
Indirect emission
|
4.858E + 08
|
Dust
|
Direct emission
|
130.70
|
Indirect emission
|
1506
|
Coal transportation
|
SO2
|
51.36
|
NO2
|
14.75
|
Dust
|
64.48
|
Coal liquefaction
|
SO2
|
934
|
Chen, 2016
|
NO2
|
1900
|
Dust
|
46900
|
4.2 Discussion on probabilistic risk models
Exposure assessment is an important procedure in probabilistic risk models, where exposure level, exposure route, and the frequency of the human body exposed to pollutants need to be defined. Although it is researched that the pathways of people exposed to environmental pollution are classified into inhalation, oral intake, and dermal intake. However, the workers in the coal-based clean energy industry are usually equipped with staff uniforms that leave a tiny area of bare skin. Furthermore, behaviors such as drinking and eating are prohibited in the working periods, thus preventing the oral intake of environmental emissions to a great extent. Therefore, the probabilistic risk assessment in this study only considers the inhalation pathway. In the following, the probabilistic risks assessment for the case study of coal mining is selected as a sample to discuss. Table 7 presents the exposure parameters reflecting the features of workers at coal mining plant, the probabilistic risks can be accordingly assessed with the application of formulas (1)-(3).
Table 7 Exposure parameters for assessing inhalation health risks.
Parameter
|
Distribution
|
Value
|
Source
|
IR
|
T
|
0.95, 1.90, 2.85
|
MEEC, 2013
|
ED
|
T
|
5, 20, 33
|
This study
|
EF
|
T
|
229, 274, 332
|
ET
|
T
|
3, 5.2, 8.5
|
BW
|
N2
|
66.32 ± 4.88
|
AT
|
T
|
1825, 7300, 12045
|
With the application of this evaluation paradigm, the dust-induced health risks in coal mining were quantified. To go into greater details, 21 types of workers involved in the life cycle of coal mining were determined, and the health risks for them are illustrated in Fig. 4. As mentioned in Sect. 2.2, the acceptable range of health risks proposed by the USEPA is 1.0E-06 to 1.0E-04, suggesting that the health risks induced by respirable dust are in the tolerable scope in most cases. Perhaps most remarkable, roadheader driver at driving face suffered from the highest risk caused by silica dust, with the average risk of 5.60E-06. What’s more, the total health risks of four workplaces were 1.44E-05, 2.41E-05, 1.43E-05, and 2.28E-05 for coal face, driving face, shotcreting point, transshipment point, respectively. And the transshipment point had the highest health risks level of dust, it can be attributed that this worksite takes on the task of underground coal transportation. More specifically, there are vertical drops between transshipment points, leading dust spread to surrounding circumstances from coal flow and thus causing greater dust pollution.
From what we have discussed above, we could easily find that with the utilization of probabilistic risk models, the health risks caused by contamination substances can be characterized in unified and comprehensible indicators. In doing so, it would be more evidence-based for the related enterprises and departments when controlling the pollutants discharging. Meanwhile, the occupants in the coal-based clean energy industry would be much clearer about the damaging degree of their worksites, and further instruct them to adopt standard protective measures.
4.3 Discussion on health impacts models
Compared with single health risk values, indicators of life loss and economic loss are often easier to understand for employees and companies when perceiving the damage extent. The third part of this evaluation paradigm, namely, health impacts models consequently play the role in transforming health risk values into more understandable indexes.
For the case study of coal mining, the probability distribution of dust-induced health impacts is illustrated in Fig. 5, suggesting that the coal mine dust had different influences on occupants in different workplaces. The highest health impacts took place at driving face, with the maximum value of 2.50 a, and following a lognormal distribution of 1.76 ± 0.14 a. As for the second high-impact worksite, the dust health impacts values at coal face ranged from 1.50 to 1.92 a, suggesting a high potential health effect. By way of contrast, the health impacts levels of coal mine dust in transshipment point and shotcreting point were slightly smaller, with the mean values of 1.24 and 0.99 a, respectively. In other words, the dust in these two places would not result in a significant hazard to human bodies, but the health impairments are still nonnegligible. Consequently, countermeasures should be taken in priority at driving face and coal face to decline the adverse health effect of dust.
As for coal-fired power generation, to present detailed results from more perspectives, comparison on the assessment results of four substages, ten terminal disease, and seven airborne pollutants were clarified. Firstly, the life loss induced by airborne pollutants in different production stages were illustrated in Fig. 6. It can be seen that the substage of coal combustion exposed to the highest health impact, following by coal mining, coal transportation, and slag disposal, with the life loss values of 118.85, 30.90, 4.14, and 0.91 a, respectively. Specifically, the total health impacts of four processing substages can be partitioned into more details. Firstly, the contribution to the total health impacts of seven pollutants is illustrated in Fig. 7(a). It is found that although the economic losses caused by various pollutants were generally different from four substages, there were still regular patterns can be summarized. On the one hand, SO2, NO2, and PM10 were always the three pollutants with the highest health impacts throughout the four processing stages. On the other hand, among the remaining four pollutants, CO2 always brought about higher damage than the other three harmful substances. For instance, during coal combustion, the WTP values of SO2, NO2, and PM10 were 1.79E + 07, 6.45E + 06, and 2.57E + 06, respectively; CO2 contributed nearly 6.71E + 05 yuan of health impacts, while N2O, CO, and CH4 only induced health impacts of 6.19E + 02, 1.99E + 02, and 1.08E + 02, severally. Furthermore, the assessment results can be explained in terms of terminal diseases as well. Notably, the terminal diseases of CH4, CO2, N2O, and CO were summarized as global warming-related diseases, and for the other four substances, their corresponding diseases were divided into two kinds, circulatory system damage and respiratory system damage. As shown in Fig. 7(b), respiratory system damage was the most serious damage type throughout the life cycle of coal-fired power generation, with the WTP values of 8.06E + 07; while circulatory system damage and global warming-related diseases contributed less, valuing as 9.10E + 06 and 7.18E + 05, respectively.
For the third case study, its health impacts induced by dust, SO2, and NO2 in the whole life cycle of coal liquefaction were assessed as well. As depicted in Fig. 8, substage of coal mining and processing contributed to the most of the economic loss, with the values of 4.28E + 04, following by coal liquefaction and coal transportation, valuing as 1.34E + 03 and 1.85E + 00, respectively. Further, for the specific airborne pollutant, the health impacts of dust were greater than SO2 and NO2 in most cases. However, during coal mining and processing, the economic loss of NO2 was bigger than dust, which enlightens us to pay attention to the NO2 pollution in this substage. To go into details, the life loss of dust and toxic and harmful gases were illustrated separately. The terminal diseases caused by dust pollution are presented in Fig. 9(a), it could be easily found that the life loss of four diseases followed the order of CWP > COPD > CVD > ARI. What’s more, as shown in Fig. 9(b), the most serious damage of toxic and harmful gases occurred in coal mining and processing, where NO2 contributed the most with the life loss value of 2.83E-01.
From what have been discussed above, it can be concluded that the health impacts models could aid to identify the terminal disease which occupies the most serious impairment. On the other hand, the evaluation indicators of DALY and WTP have meanings of life and economic loss, respectively. There is no doubt that compared with the pollutant concentrations and health risks, enterprises and workers would be more sensitive to these two indexes, and could provide references for the policy formulation on environmental taxes and health subsidies.
4.4 Comparative analysis
Keeping paces with the development of coal-based clean energy industry, the related environmental pollution issues have recently been a hot topic in academia, and many contributory studies are conducted. In this section, we work on reviewing the research methods mainly applied in the related studies.
After taking close insight into the related researches in this area, the authors found that these publications can be partitioned as three hot topics. Firstly, to investigate the relationship between coal consumption and environmental contamination, most studies apply the dataset of pollutants concentration and the distribution of coal consumption (Xie et al., 2020). Herein, establishing the exposure-response relationship between different air pollution and health effect ends is a research focus. For instance, with the established exposure-response relationship, Li et al. (2018a) measured the number of different health effect ends caused by PM2.5 emissions of coal consumption under various emission scenarios. Secondly, simulation physical models are commonly utilized to simulate air conditions during clean coal energy processing. Such examples might be given easily, Xiu et al. (2020) established a highly simulated physical model of the goaf and multiple dust-removal air flow rates in the roadway, to investigate the dust pollution characteristics during coal mine production. Likely, Chen et al. (2020b) employed a three-dimensional nested air quality condition model with source apportionment to analyze the environmental impacts of coal-fired power plants. Amongst, it is noteworthy that the majority of researchers have recognized the processing of coal-based clean energy industry includes numerous resource consumption and emission inventories. Therefore, the idea of LCA, which can evaluate environmental pollution burden from “cradle-to-grave” process of a product or service, is often used (Wu et al., 2017; Zhang et al., 2018; Ghadimi et al., 2019). Thirdly, as it is a common knowledge that the road to sustainable development is only attainable if it is built on the simultaneous development among environment, economic, and social. Correspondingly, when discussing pollutant discharging and its health impacts in the coal-based clean energy industry, many researchers combine with the considerations of economic and technical analysis (Cui et al., 2018; Zhao et al., 2019; Yang et al., 2020). Meanwhile, increasing numbers of studies start to adopt economic cost and health benefits as the final assessment indicators, to turn the assessment results more intuitive and understandable. For example, Chen et al. (2020a) estimated the impacts on public health and the related economic loss of PM2.5 pollution produced by coal consumption using the Poisson regression model.
As a whole, many kinds of innovative and effective methods have emerged to explore the contamination issues in the coal-based clean energy industry. However, there are still some gaps that need to be filled. On the one hand, pollution data in dataset usually report the statistical condition, scene sampling would be more correct to reflect the contamination at specific worksites. On the other, although the ideologies of life cycle and economic indexes are used extensively, they often serve to technical assessment and isolate from environmental evaluation. Correspondingly, the evaluation paradigm we proposed could assess environmental impacts from the perspectives of life and economic loss, and the idea of LCA was combined. It is believed that this paradigm could fill the gap and provide references for academia and industrial development to some extent.