Installation Planning in Regional Thermal Power Industry for Emissions Reduction Based on an Emissions Inventory

International Journal of Environmental Research and Public Health

Article Installation Planning in Regional Thermal Power Industry for Emissions Reduction Based on an Emissions Inventory

Yu Zhang 1, Jiayu Wu 2, Chunyao Zhou 1 and Qingyu Zhang 1,* 1 Department of Environmental Engineering, Zhejiang University, Hangzhou 310058, China; [email protected] (Y.Z.); [email protected] (C.Z.) 2 Appraisal Center for Environment and Engineering, Ministry of Environmental Protection, Beijing 100012, China; [email protected] * Correspondence: [email protected]

Received: 21 February 2019; Accepted: 11 March 2019; Published: 15 March 2019

Abstract: Exploring suitable strategies for air pollution control, while still maintaining sustainable development of the thermal power industry, is significant for the improvement of environmental quality and public health. This study aimed to establish a coupling relationship between installed capacity versus energy consumption and pollutant emissions, namely the installed efficiency, and to further provide ideas and methods for the control of regional air pollutants and installation planning. An inventory of 338 installed thermal power units in the Jing-Jin-Ji Region in 2013 was established as a case study, and comparisons were made by clustering classification based on the installed efficiencies of energy consumption and pollutant emissions. The results show that the thermal power units were divided into five classes by their installed capacity: 0–50, 50–200, 200–350, 350–600, and 600+ MW. Under the energy conservation and emissions reduction scenario, with the total installed capacity and the power generation generally kept constant, the coal consumption was reduced by 17.1 million tons (8.7%), and the total emissions were reduced by 79.8% (SO2), 84.9% (NOx), 60.9% (PM), and 59.5% (PM2.5).

Keywords: thermal power industry; air pollution; emissions inventory; installation planning

1. Introduction Currently, thermal power is still an indispensable part of the electric power industry. The world’s total thermal power generation was 9.41 × 1011 GWh in 2015, which will continue to increase in the following 10 years and is expected to reach1.01 × 1012 GWh in 2025 [1]. However, air pollution from thermal power industry is an important cause of haze, being responsible for 35% of haze in China [2], with an annual 1% increase exposure to ﬁne particulate matter (PM2.5) corresponding to a 2.942% increase in household health expenditure [3]. Previous studies have stated that the introduction of air control policies can help with the improvement of environment quality and public health [4,5]. Therefore, exploring suitable strategies for air pollution control, while maintaining sustainable development of the thermal power industry, is currently one of the issues receiving attention. It has been shown that, in some industries, air pollutant emissions from 30% of pollution sources with the most outdated processes and technology account for 58%–82% of the industry’s total [6]. To determine the key areas to control emissions, and ultimately provide a solution to pollution strategies and installation planning, it is prudent to explore whether the aspects of low production capacity and air pollution also prevail in the thermal power sector. To date, many emission inventories of pollutant sources have been established and studied to develop emissions reduction strategies in China and other countries [7–10]. However, these

Int. J. Environ. Res. Public Health 2019, 16, 938; doi:10.3390/ijerph16060938 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 938 2 of 13 inventories analyzed the emissions reduction strategies based only on the level of emissions; even if the production scale was taken into account, no relationship was established between pollutant emissions and production scale [9]. Hence, after an emissions inventory is established, it is still necessary to establish a coupling relationship between production capacity and pollutant emissions, determine priorities for emissions reduction, and propose a reasonable emissions reduction strategy. The Lorenz curve is generally used to illustrate the distribution of national income among a country’s population, and its function is to visualize the unequal relation of two variables [10]. In recent years, the types of variables illustrated by the Lorenz curve have become more extensive. This curve has been used to establish unequal relations between socio-economic variables, such as population, income, and gross domestic product, versus pollutant emissions, as well as in environmental fields, such as carbon emissions reduction [11–13]. The Gini coefficient can comprehensively examine the degree of distribution equality, because the curvature of the Lorenz curve is proportional to the Gini coefficient. An emissions reduction strategy may create inequality if it is based solely on the level of pollutant emissions without taking into account the impact of equipment and technology. Thus, the Lorenz curve can be used to analyze emissions reduction strategies in the thermal power industry by considering equipment, technology, and energy consumption, while the Gini coefficient can be adopted to estimate the inequality index of emissions from thermal power units. K-means clustering is a typical partition clustering algorithm that has been applied to determine the air index, and the association between air pollution and human health [14–16]. Owing to its simplicity and efficiency, k-means clustering is suitable for rapid grouping under the impact of multiple factors. Because the effects of three factors, namely installed capacity, coal consumption, and various pollutant emissions, should be taken into account for emissions reduction strategies in the thermal power industry, k-means clustering enables rapid grouping of installations according to the pollutant emissions inventory. This paper aims to establish a coupling relationship between installed capacity versus energy consumption and pollutant emissions, and to further provide ideas and methods for the control of regional air pollutants and installation planning. The thermal power industry in the Beijing-Tianjin-Hebei (Jing-Jin-Ji) Region was selected for case analysis of SO2, NOx, and particulate matter (PM) emissions in the year 2013. We established an inventory of the production scale, coal consumption, and air pollutant emissions, and determined the inequality by using the Lorenz curve. A coupling relationship between installed capacity versus energy consumption and pollutant emissions was then established, namely the installed efficiency. Further, we used k-means clustering to classify thermal power units by their installation scale, analyzed the emission characteristics, and devised an energy conservation and emissions reduction (EE) scenario, thereby determining industry shutdown limits, entry thresholds, and industry upgrades based on the efficiency of pollutant emissions

2. Methods

2.1. Data Collection The geographic location, unit information, coal consumption, ﬂue gas emissions, pollutant control, and stack parameters of thermal power units in the Jing-Jin-Ji Region were collected and improved through the cooperation of the Environmental Engineering Assessment Center of the Ministry of Environmental Protection, local environmental protection departments, and the power generation groups. In addition, the database was checked and supplemented by a combination of ﬁeld investigation and questionnaires. The details are shown in Table1. Int. J. Environ. Res. Public Health 2019, 16, 938 3 of 13

Table 1. Information gathering methods.

Information Category Speciﬁc Description Geographic location City, province and longitude, latitude (◦/0/00) Unit number, installation thermal capacity, boiler Unit information tonnage, construction conditions, construction property, and power generation hours Standard coal consumption for power generation, coal consumption, coal quality information (ash Coal consumption construction content, sulfur content, nitrogen content, mercury content, coal source, and caloriﬁc value)

Flue gas volume, SO2 concentration, SO2 emission Flue gas emissions rate, NOx concentration, NOx emission rate, PM concentration, and PM emission rate Treatment process, treatment efﬁciency, emission Pollutant control concentration, and emission load Height, outlet diameter, outlet temperature, ﬂow rate, Chimney parameters and emission pattern

2.2. Establishment and Verification of Emissions Inventory The emissions inventory was established based on pollution source survey methods to collect basic information, such as exact location, size, coal consumption, and power generation hours of coal-fired power-generating units in the Jing-Jin-Ji Region. The emissions factor method was used to calculate the emissions and emission rates from coal-fired units according to the mean hourly concentration of specific pollutant emissions, flue gas volume under standard conditions, and power generation hours during the study year. The pollutant emissions of SO2, NOx, and PM were calculated as follows: = St × × −6 Q ∑i=1 Ci Li 10 (1) where Q is the annual emission of a pollutant in kilograms per hour, Ci is the mass concentration of hourly emissions of dry flue gas under standard conditions in milligrams per cubic meter, Li is the dry flue gas volume under standard conditions in cubic meters per hour (here, Li is estimated based on the coal consumption of 8.5–9 m3/kg according to the installation class), and St is the power generation hours within one year. Because PM2.5 monitoring was not specifically performed, PM2.5 emissions were quantified based on their mass ratio in PM (the ratio gradually increased with decreasing PM concentration) to derive final emissions values. According to the practical investigation, the actual total installed capacity of the thermal power industry in the Jing-Jin-Ji Region was 54,801 MW. This result was 2% lower than that recorded in the statistical yearbook, mainly because some enterprises had no production. According to the investigation and verification, the total emissions from thermal power units in this region were 298,431.18 (SO2), 60,321.190 (NOx), and 72,007.47 (PM) tons, while the values were 311,706.7 (SO2), 709,217.7 (NOx), and 85,140.7 (PM) tons, according to the Annual Statistic Report [17]. The probable reason is that the statistics of some thermal power plants in the region were not accurately recorded.

2.3. Establishment of Unequal Relations To analyze the inequality of pollutant emissions from different scales of thermal power units, the units were ranked by size in descending order. We calculated the ratio of cumulative capacity Y(i) and Int. J. Environ. Res. Public Health 2019, 16, 938 4 of 13

the cumulative ratio X(i) of SO2, NOx, PM, and PM2.5 emissions. A Lorenz curve for installed capacity versus pollutant emissions was drawn according to the function

Y = L(X) (2)

The Gini coefﬁcient (G) was calculated by the curve ﬁtting method using a polynomial function:

n n−1 Yˆ = Lˆ (X) = anX + an−1X + ... + a1X + a0 (3) where n = 6. The value of G was calculated using the following formula of integration:

Z 1 Z 1 G = 1 − 2 L(X)dX ≈ 1 − 2 Lˆ (X)dX (4) 0 0

2.4. Installed Efﬁciency To establish the relationship between production scale versus pollutant emissions and coal consumption, we calculated the SO2, NOx, PM, and PM2.5 emissions and coal consumption per unit installed capacity, that is, the installed efﬁciency:

Pij Eij = (5) Cij where Eij is the installed efﬁciency of thermal power units with installed capacity I in the range of i–j (i ≤ I < j); Cij is the total installed capacity of thermal power units with installed capacity in the range of i–j; and Pij is the annual emissions of SO2, NOx, PM, and PM2.5 and coal consumption in thermal power units with installed capacity in the range of i–j.

2.5. K-Means Clustering Analysis The SPSS statistical analysis software (IBM SPSS, Somers, NY, USA) was used to perform k-means clustering for thermal power enterprises classified into different installation scales. The variable for installed efficiency was calculated at 50 MW intervals. The total installed capacity of units with 400–450, 450–500, and 550–600 MW of thermal power was 0, and thus their installed efficiency was also 0, and these sizes were not included in the clustering process. The number of clusters was determined using the elbow method. The maximum number of iterations was 10 and the convergence criterion was 0.02.

2.6. Case Study The Jing-Jin-Ji Region is located in the western part of China’s Bohai Economic Rim, at the northern end of the North China Plain. In this region, there are 11 prefecture-level cities, including Beijing, Tianjin, and Hebei, and it has a total area of 21.80 × 104 km2. This region has experienced one of the fastest economic developments in the world over the past 30 years, and it is also one of the regions with the most serious air pollution in China. In January 2013, the Jing-Jin-Ji Region experienced the worst persistent haze pollution incident in history [18]. That same year, the Ministry of Environmental Protection of China announced the top 10 cities with most serious air pollution [19], 8 of which are in the Jing-Jin-Ji Region. Thermal power units in this region are mainly concentrated in the North China Plain where terrain is ﬂat, especially in the southern area and the eastern coast of this region (Figure1). Int. J. Environ. Res. Public Health 2019, 16, 938 5 of 13 Int. J. Environ. Res. Public Health 2019, 16, x 5 of 13

Figure 1. Distribution of thermal power unitsunits in the Jing-Jin-Ji Region.

The Jing-Jin-JiJing-Jin-Ji Region Region has has numerous numerous installations installations for China’s for China’s thermal powerthermal industry. power Accordingindustry. toAccording the statistical to the yearbook statistical [20 yearbook], the thermal [20], powerthe thermal installed power capacity installed in Jing-Jin-Ji capacity Region in Jing-Jin-Ji was 59.70 Region GW inwas 2013, 59.70 that GW is, in 6.9% 2013, of thethat country’s is, 6.9% of installed the countr thermaly’s installed power capacity; thermal yetpower this onlycapacity; accounts yet this for 2.3%only ofaccounts the country’s for 2.3% total of the area. country’s From 2005 total to area. 2013, From thermal 2005 power to 2013, installed thermal capacity power grew installed by 84.6% capacity and thermalgrew by power84.6% and generation thermal grew power by generation 66.8% in the grew Jing-Jin-Ji by 66.8% Region. in the Jing-Jin-Ji Region. As the thermal power industry is the main coal-consuming industry in the Jing-Jin-Ji Region, its pollutant emissions have a profound impact on regional air quality. A recent study [[21]21] showed that emissions from thermal power industry in the Jing-Jin-Ji Region accounted for 25.02% (SO22), 39.55% (NOxx), and 5.73% 5.73% (PM 10,, inhalable inhalable particles, particles, <10 <10 μµm)m) of of the the region’s region’s total total emissions emissions of of these these pollutants. pollutants. According to to the the Environmental Environmental Quality Quality Bulletin Bulletin of the of theBeijing-Tianjin-Hebei Beijing-Tianjin-Hebei Region Region [22–24], [22 –in24 2013,], in 2013,the mean the meanannual annual concentrations concentrations of pollutants of pollutants such as suchNO2, asPM NO2.5 (fine2, PM particles,2.5 (ﬁne particles,<2.5 μm), <2.5and PMµm),10 and(excluding PM10 (excluding SO2) exceeded SO2) the exceeded national the standards national standards in Beijing in and Beijing Tianjin, and while Tianjin, the while mean the annual mean annualconcentrations concentrations of all pollutants of all pollutants exceeded exceeded the national the national standards standards in inHebei Hebei Province. Province. Thus, Thus, it it is representative to select the thermal power industry in the Jing-Jin-Ji Region for case analysis in the year 2013.

3. Results and Discussion

3.1. Production Scale and Emissions Data There werewere 133133 thermalthermal powerpower enterprises enterprises (groups) (groups) in in the the Jing-Jin-Ji Jing-Jin-Ji Region Region in in 2013, 2013, with with a totala total of 338of 338 installed installed units units and and a total a total installed installed capacity capacity of 54,800.5 of 54,800.5 MW. TheMW. number The number of installed of installed thermal thermal power unitspower per units unit per area unit and area the and installed the installed capacity capacity in different in different cities are cities detailed are indetailed Figure2 ina,b. Figure The installed2(a) and capacity2(b). The of installed thermal capacity power units of thermal in Tianjin, power Tangshan, units in Xingtai, Tianjin, and Tangshan, Handan wasXingtai, far greater and Handan than that was in far greater than that in other cities. As is shown in Figure 1, these areas have flat terrain with more plains than mountains, and therefore are suitable for the construction of thermal power units.

Int. J. Environ. Res. Public Health 2019, 16, x 6 of 13

The total coal consumption of the thermal power industry in the Jing-Jin-Ji Region was 196.3914 million tons, accounting for 10.34% of the total thermal coal consumption in China [20]. The spatial distribution of thermal power coal consumption per unit area, as shown in Figure 2(c), was consistent with that of the installed level in the Jing-Jin-Ji Region. The total emissions of the thermal power Int.industry J. Environ. in Res. Jing-Jin-Ji Public Health Region2019, 16 were, 938 146,791.87 (SO2), 351,303.49 (NOx), 36,712.58 (PM), and 18,551.326 of 13 (PM2.5) tons. The pollutant emissions of different cities are shown in Figure 2(d). other cities. As is shown in Figure1, these areas have ﬂat terrain with more plains than mountains, and therefore are suitable for the construction of thermal power units.

Figure 2. Spatial distribution of thermal power units in the Jing-Jin-Ji Region: (a) installations per unit −3 2 b 2 c areaFigure (10 2. units/kmSpatial distribution), ( ) installed of thermal capacity power per units unit areain the (kW/km Jing-Jin-Ji), Region: ( ) coal consumption(a) installations per per unit unit area (ton/km2/year), (d) annual emissions (10−3 ton/year). area (10−3 units/km2), (b) installed capacity per unit area (kW/km2), (c) coal consumption per unit area (ton/km2/year), (d) annual emissions (10−3 ton/year). The total coal consumption of the thermal power industry in the Jing-Jin-Ji Region was 196.3914 million tons, accounting for 10.34% of the total thermal coal consumption in China [20]. 3.2. Unit Classification The spatial distribution of thermal power coal consumption per unit area, as shown in Figure2c, was consistentThe withLorenz that curve of the of installedpollutant levelemissions in the in Jing-Jin-Ji Jing-Jin-Ji Region. Region The is shown total emissionsin Figure 3. of The the emissions thermal powerof thermal industry power in Jing-Jin-Jiunits with Region Y(i) > were80% accounted 146,791.87 for (SO 39.7%2), 351,303.49 (SO2), 34.0% (NO x(NO), 36,712.58x), 34.7% (PM), (PM), and and 18,551.3232.8% (PM (PM2.52.5) )of tons. the Thetotal pollutant emissions. emissions That is, of the different SO2 emissions cities are shownfrom the in Figure smallest2d. 20% of units contributed to 39.7% of the total emissions. 3.2. Unit Classiﬁcation The Lorenz curve of pollutant emissions in Jing-Jin-Ji Region is shown in Figure3. The emissions of thermal power units with Y(i) > 80% accounted for 39.7% (SO2), 34.0% (NOx), 34.7% (PM), and 32.8% (PM2.5) of the total emissions. That is, the SO2 emissions from the smallest 20% of units contributed to 39.7% of the total emissions.

Int. J. Environ. Res. Public Health 2019, 16, 938 7 of 13 Int. J. Environ. Res. Public Health 2019, 16, x 7 of 13

Figure 3. Lorenz curve of thermal power unitsunits inin Jing-Jin-JiJing-Jin-Ji Region.Region.

ByBy usingusing thethe curvecurve fittingfitting method,method, the GiniGini coefficientscoefficients ofof emissionsemissions were calculatedcalculated to bebe 0.280.28 (SO(SO22), 0.22 (NO(NOx),), 0.23 (PM), and 0.210.21 (PM(PM2.52.5),), showing showing a significant significant inequality of emissions. Hence, thethe thermalthermal powerpower unitsunits neededneeded toto be classifiedclassified to study pollutant emissions and emissions reduction strategiesstrategies forfor differentdifferent classes.classes. TheThe elbowelbow methodmethod determineddetermined the number of clustersclusters to bebe 5,5, andand thenthen k-meansk-means clusteringclustering waswas performed.performed. Results are given in Table2 2.. BecauseBecause nono unitsunits hadhad anan installedinstalled capacitycapacity ofof 400–450,400–450, 450–500,450–500, oror 550–600550–600 MW thermal power, the fourth class waswas combined into the 350350 toto 600600 MWMW class.class. InIn thisthis way,way, thethe thermalthermal powerpower unitsunits werewere divideddivided intointo fivefive classesclasses byby theirtheir installationinstallation scale:scale: 0–50, 50–200, 200–350,200–350, 350–600,350–600, andand 600+600+ MW.

Table 2.2. K-means clustering of installed efﬁciency.efficiency. Installed Coal Consumption SO NO PM PM Coal 2 x 2.5 Cluster InstalledCapacity (103 ton/MW) SO(ton/MW)2 (ton/MW)NOx (ton/MW)PM (ton/MW)PM2.5 Consumption (103 Cluster Capacity (ton/MW) (ton/MW) (ton/MW) (ton/MW) 0–50ton/MW) 0.87 27.76 34.04 5.45 2.3 1 50–100 0.58 10.3 15.07 2.63 1.18 2 0–50100–150 0.87 0.54 27.76 9.92 34.04 13.1 5.45 1.78 2.3 0.87 21 50–100150–200 0.58 0.53 10.3 10.05 15.07 14.69 2.63 2.45 1.18 1.06 22 100–150200–250 0.54 0.46 9.92 4.94 13.1 13.8 1.78 1.16 0.87 0.6 52 150–200250–300 0.53 0.4 10.05 4.84 14.69 12.33 2.45 1.17 1.06 0.62 52 300–350 0.36 5.13 11.82 1.29 0.65 5 200–250 0.46 4.94 13.8 1.16 0.6 5 350–400 0.31 3.58 11.71 1.05 0.53 4 250–300400–450 *0.4 - 4.84 -12.33 - 1.17 - 0.62 - -5 300–350450–500 *0.36 - 5.13 -11.82 - 1.29 - 0.65 - -5 350–400500–550 0.31 0.35 3.58 2.93 11.71 12.08 1.05 1.05 0.53 0.52 44 400–450550–600 * *------600–650 0.27 3.54 6 0.83 0.42 3

450–500650–700 * - 0.26 - 3.18- 5.28 0.81- 0.4- 3- 500–5501000+ 0.35 0.21 2.93 1.71 12.08 6.09 1.05 0.57 0.52 0.29 34

550–600* The total * installed capacity- is 0 in 400–450, - 450–500, and 550–600- MW, so theinstalled efﬁciency- does not exist.- 600–650 0.27 3.54 6 0.83 0.42 3 3.3. Emission650–700 Characteristics0.26 3.18 5.28 0.81 0.4 3 1000+ 0.21 1.71 6.09 0.57 0.29 3 The* The pollutant total installed emissions capacity and is 0 theirin 400–450, ratio 450–500, for each and class 550–600 of units MW, are so shown the inst inalled Figure efficiency4. The does largest pollutantnot exist. emissions level was found in thermal power units of 200–350 MW, where the total emissions were 146,791.87 (SO2), 351,303.49 (NOx), 36,712.58 (PM), and 18,551.32 (PM2.5) tons, with corresponding emission ratios of 49.2% (SO2), 58.2% (NOx), 51.0% (PM), and 52.9% (PM2.5). The installed capacity

Int. J. Environ. Res. Public Health 2019, 16, x 8 of 13

3.3. Emission Characteristics

Int.The J. Environ. pollutant Res. Public emissions Health 2019 and, 16 their, x ratio for each class of units are shown in Figure 4. The 8largest of 13 pollutant emissions level was found in thermal power units of 200–350 MW, where the total emissions3.3. Emission were Characteristics146,791.87 (SO 2), 351,303.49 (NOx), 36,712.58 (PM), and 18,551.32 (PM2.5) tons, with correspondingThe pollutant emission emissions ratios andof 49.2%their ratio (SO for2), 58.2%each cl ass(NO ofx), units 51.0% are (PM),shown and in Figure 52.9% 4. (PMThe 2.5largest). The installedpollutant capacity emissions of this level class was of units found accounted in thermal for power53.1% ofunits the totalof 200–350 installed MW, capacity, where which the total was comparableemissions to were the emission146,791.87 ratios. (SO2), In351,303.49 addition, (NO thex ),installed 36,712.58 capacity (PM), and of 0 18,551.32 to 50 MW (PM units2.5) tons,accounted with for corresponding3.4%Int. of J. Environ. the installed Res.emission Public Healthcapacity, ratios2019, 16of ,and 93849.2% their (SO total2), 58.2% emissions (NOx ),were 51.0% 52,060.57 (PM), and (SO 52.9%2), 63,837.98 (PM8 of2.5 13 ).(NO Thex), 10,219.44installed (PM), capacity and 4306.38of this class (PM of2.5 )units tons, accounted with corresponding for 53.1% of emission the total ratiosinstalled of 17.4%,capacity, 10.6%, which 14.2%, was andcomparable 12.3%.of this According class to ofthe units emission to accountedthese ratios. results, for In 53.1% theaddition, ofeffect the total theof emissions installed installed capacity,capacity reduction which of 0by to was shutting 50 comparableMW units down toaccounted units the <50 MWfor was 3.4%emission significant. of the ratios. installed In addition, capacity, the installedand their capacity total emissions of 0 to 50 MWwere units 52,060.57 accounted (SO2 for), 63,837.98 3.4% of the (NOx), 10,219.44installed (PM), capacity, and 4306.38 and their (PM total2.5 emissions) tons, with were corresponding 52,060.57 (SO2), emission 63,837.98 ratios (NOx), of 10,219.44 17.4%, (PM),10.6%, and 14.2%, and 4306.3812.3%. (PMAccording2.5) tons, withto these corresponding results, the emission effect of ratios emissions of 17.4%, reduction 10.6%, 14.2%, by andshutting 12.3%. down According units <50 MWto was these significant. results, the effect of emissions reduction by shutting down units <50 MW was signiﬁcant.

Figure 4. Pollutant emissions and ratios for each class.

Figure 4. Pollutant emissions and ratios for each class. 3.4. Installed Efficiency Figure 4. Pollutant emissions and ratios for each class. The3.4. installed Installed Efficiencyefficiency of different classes of thermal power units is shown in Figure 5. There 3.4. Installed Efficiency were markedThe differences installed efficiency in the of efficiency different classes of po ofllutant thermal emissions power units and is shown coal inconsumption Figure5. There among were the variousmarked Theclasses. installed differences In other efficiency inwords, the efficiency of when different ofconverted pollutant classes emissionstoof thethermal same and power scale coalconsumption ofunits power is shown generation among in theFigure variouscapacity, 5. There the coalwere consumptionclasses. marked In differences other and words,pollutant in when the emissions efficiency converted of toof small thepollutant same units scale emissionswere of much power and greater generation coal consumptionthan capacity, those of the amonglarge coal units. the consumption and pollutant emissions of small units were much greater than those of large units. Forvarious 0–50 MW classes. of installed In other capacity, words, when the installed converted efficiency to the same reached scale 27.76 of power (SO2 ),generation 34.04 (NO capacity,x), 5.45 (PM), the For 0–50 MW of installed capacity, the installed efficiency reached 27.76 (SO2), 34.04 (NOx), 5.45 (PM), andcoal 2.30 consumption (PM2.5) kilotons/MW. and pollutant With emissions 600+ MW of installed small units capa werecity, much the installed greater than efficiency those of was large only units. 3.21 and 2.30 (PM2.5) kilotons/MW. With 600+ MW installed capacity, the installed efficiency was only 3.21 (SOFor2), 5.83 0–50 (PM), MW of0.80 installed (PM), capacity,and 0.40 the(PM installed2.5) kilotons/MW. efficiency reached 27.76 (SO2), 34.04 (NOx), 5.45 (PM), (SO2), 5.83 (PM), 0.80 (PM), and 0.40 (PM2.5) kilotons/MW. and 2.30 (PM2.5) kilotons/MW. With 600+ MW installed capacity, the installed efficiency was only 3.21 (SO2), 5.83 (PM), 0.80 (PM), and 0.40 (PM2.5) kilotons/MW.

3 Figure Figure5. Installed 5. Installed capacity. capacity. * The * The unit unit of ofinstalled installed efficiency efﬁciency for for coal coal consumption consumption is 10 iston/MW, 10 3 ton/MW, and and the unit of installed efﬁciency for pollutants is ton/MW. the Figureunit of 5. installed Installed efficiency capacity. *for The pollutants unit of installed is ton/MW. efficiency for coal consumption is 103 ton/MW, and the unit of installed efficiency for pollutants is ton/MW.

Int. J. Environ. Res. Public Health 2019, 16, x 9 of 13 Int. J. Environ. Res. Public Health 2019, 16, 938 9 of 13 The relationships between the installed efficiency of different classes versus the total installed capacity and mean installed efficiency are illustrated in Figure 6. By comparing the installed efficiency and theThe total relationships installed capacity between of the different installed classes, efficiency it was of differentderived that classes the versusunits with the total200–350 installed MW thermalcapacity power and mean had installed the highest efficiency total installed are illustrated capacity, in Figure while6 .their By comparing installed efficiency the installed of efficiencypollutant emissionsand the total and installed coal consumption capacity of was different comparable classes, to it the was average derived level that and the ranked units with third. 200–350 Therefore, MW reducingthermal powerthe overall had theinstalled highest efficiency total installed of 200–350 capacity, MW whilethrough their a process installed improvement efficiency of approach pollutant wouldemissions significantly and coal consumptionsave energy and was reduce comparable emissions. to the The average remaining level andfour rankedclasses third.were units Therefore, with 600+,reducing 350–600, the overall 50–200, installed and 0–50 efficiency MWh, in ofdescending 200–350 MW order through according a process to the improvementtotal installed approachcapacity. Theirwould corresponding significantly saveinstalled energy efficiency and reduce rankings emissions. were fourth, The remaining fifth, second, four and classes first. were It is unitsnecessary with to600+, limit 350–600, the production 50–200, andof units 0–50 with MWh, 0–350 in descending MW and encourage order according the construction to the total of installed new units capacity. with 600+Their MW. corresponding By comparing installed the installed efficiency efficiency rankings and were the fourth,mean installed fifth, second, efficiency and of first. all classes, It is necessary it was derivedto limit thethat productionthe installed of efficiency units with of 0–350 pollutan MWt emissions and encourage and coal the consumption construction ofin newthermal units power with units600+ MW.with By350+ comparing MW was the below installed the average efficiency level. and The the meanprobable installed reason efficiency is that technologies of all classes, of it wasthe thermalderived thatpower the units installed with efficiency 0–350 MW of pollutantare limited emissions and backward. and coal consumptionThus, it is more in thermal conducive power to achievingunits with the 350+ EE MWgoals was to restrict below thethe averageinstalled level. capacity The of probable new thermal reason power is that units technologies to no less of than the 350thermal MW. power units with 0–350 MW are limited and backward. Thus, it is more conducive to achieving the EE goals to restrict the installed capacity of new thermal power units to no less than 350 MW.

Figure 6. Cont.

Int. J. Environ. Res. Public Health 2019, 16, 938 10 of 13 Int. J. Environ. Res. Public Health 2019, 16, x 10 of 13

Figure 6. Installed capacity and installed efficiency: (a) installed capacity and SO2 emissions installed Figure 6. Installed capacity and installed efficiency: (a) installed capacity and SO2 emissions installed efficiency, (b) installed capacity and NOx emissions installed efficiency, (c) installed capacity and PM efficiency, (b) installed capacity and NOx emissions installed efficiency, (c) installed capacity and PM emissions installed efficiency, (d) installed capacity and PM2.5 emissions installed efficiency, (e) emissions installed efficiency, (d) installed capacity and PM2.5 emissions installed efficiency, (e) installed installedcapacity andcapacity coal consumptionand coal consumption installed installed efficiency. efficiency.

3.5. Energy Energy Conservation and Emissions Reduction Scenario An EE scenario scenario was was established established according according to the the above above analysis, analysis, combined combined with with related related standard standard bulletins in the thermal power industry [25–29], [25–29], and and compared compared with with a a “business as as usual” (BAU) (BAU) scenario for for the thermal power industry in in the Jing Jing-Jin-Ji-Jin-Ji Region in in 2013. We We assumed that the coal quality was constant and that the total power genera generationtion and total installed capacity were generally kept constant. The EE scenario ha hadd the following characteristics: (1) AllAll <50 <50 MW MW units units were were shut shut down. down. (2) TheThe 50–200 50–200 MW MW units units met met special special emission emission standa standardsrds (with (with emission emission conc concentrationsentrations no no greater 3 thanthan 50 50 (SO (SO22),), 100 100 (NO (NOxx),), and and 20 20 (PM) mg/m3),), with with a a standard standard coal coal consumption consumption for for power generationgeneration that that was was no no greater greater than than 293 293 g/kWh. g/kWh. (3) TheThe 200–350 200–350 MW MW thermal thermal power power units units met ultra-low emission standards (with emission 3 concentrationsconcentrations no no greater greater than than 35 (SO2),), 50 (NOx),), and 10 (PM)(PM) mg/mmg/m3),), with with a a standard standard coal consumptionconsumption for for power power generation generation that that was was no no greater greater than than 293 293 g/kWh. g/kWh. (4) TheThe existing existing 350–600 350–600 MW MW and and 600+ 600+ MW MW thermal thermal power power units units met met ultra-low ultra-low emission emission standards standards (as(as given given in in item item 3), 3), with with a a standard standard coal coal cons consumptionumption for for power power generation generation no no greater greater than 293 andand 284 284 g/kWh g/kWh when when installed installed capacity capacity was was increased increased by by 5% 5% and and 10%, 10%, respectively. respectively. The The newly newly installedinstalled units units met met ultra-low ultra-low emission emission standard standardss and and the the standard standard coal coal consumption consumption for for power generationgeneration by by the the new new thermal thermal power power units units was was no no greater greater than than 287 287 and and 270 270 g/kWh, g/kWh, respectively. respectively. The production scale, annual (a) coal consumption, and annual pollutant emissions of different unit classes under the BAU and EE scenarios are shown in Table3 3.. The results show that under the EE scenario, different unit classes had the following effects on the total production scale and total pollutantTable 3. emissions Scenario analysis with respect results. to the BAU scenario:

(1) InstalledShutting down theNumber 0–50 MWof Installed thermal powerPower units reducedCoal theSO installed2 NOx capacity,PM PM power2.5 Classificationgeneration, Scenario and coal Installed consumption Capacity by 3.4%, Generation 3.7%, and 8.3%,Consumption respectively; (103 the(10 total3 emissions(103 (10 were3 (MW) Units (MW) (106 MWh) (106 ton/a) ton/a) ton/a) ton/a) ton/a) reduced by 17.4% (SO2), 10.6% (NOx), 14.2% (PM) and 12.3% (PM2.5). BAU 156 1875.5 12.2 16.4 52.1 63.8 10.2 4.3 0–50 (2) For the 50–200EE MW units,0 the EE scenario 0 with 0 constant installed 0 capacity0 and0 power0 generation0 reduced coalBAU consumption 41 by 0.9%;3453.0 the total21.6 emissions by 19.0 8.6% (SO33.62), 5.8%51.3 (NO x7.7), 5.9% 3.5 (PM), 50–200 EE 41 3453.0 21.6 17.2 7.9 16.3 3.4 1.7 and 5.1% (PM2.5). BAU 105 29,082.0 174.3 105.0 146.8 351.3 36.7 18.6 (3) 200–350For the 200–350 MW units, the EE scenario with constant installed capacity and power generation EE 105 29,082.0 174.3 101.2 33.1 47.3 18.9 9.5 reduced coalBAU consumption 12 by 1.9%;4830.0 the total27.9 emissions 15.7 were reduced16.0 by46.1 38.1% 5.1 (SO 2), 50.4%2.5 350–600 (NOx), 24.7%EE (PM), and12 25.9% (PM5071.52.5). 29.3 16.5 5.2 7.4 1.5 0.7 BAU 24 15,560.0 94.0 40.3 50.0 90.7 12.4 6.2 600+ EE 25 17,116.0 103.4 44.3 14.0 19.9 4.3 2.2

Int. J. Environ. Res. Public Health 2019, 16, 938 11 of 13

(4) For the 350–600 MW units, the EE scenario increased the installed capacity, power generation, and coal consumption by 0.4%, 0.4%, and 0.4%, respectively; the total emissions were reduced by 3.6% (SO2), 6.4% (NOx), 5.0% (PM), and 5.1% (PM2.5). (5) For the 600+ MW units, the EE scenario increased the installed capacity, power generation, and coal consumption by 2.8%, 2.8%, and 2.1%, respectively; the total emissions were reduced by 12.1% (SO2), 11.7% (NOx), 11.1% (PM), and 11.4% (PM2.5).

Table 3. Scenario analysis results.

Installed Number of Installed Power Coal SO2 NOx PM PM2.5 Classiﬁcation Scenario Installed Capacity Generation Consumption (103 (103 (103 (103 (MW) Units (MW) (106 MWh) (106 ton/a) ton/a) ton/a) ton/a) ton/a) BAU 156 1875.5 12.2 16.4 52.1 63.8 10.2 4.3 0–50 EE 0 0 0 0 0 0 0 0 BAU 41 3453.0 21.6 19.0 33.6 51.3 7.7 3.5 50–200 EE 41 3453.0 21.6 17.2 7.9 16.3 3.4 1.7 BAU 105 29,082.0 174.3 105.0 146.8 351.3 36.7 18.6 200–350 EE 105 29,082.0 174.3 101.2 33.1 47.3 18.9 9.5 BAU 12 4830.0 27.9 15.7 16.0 46.1 5.1 2.5 350–600 EE 12 5071.5 29.3 16.5 5.2 7.4 1.5 0.7 BAU 24 15,560.0 94.0 40.3 50.0 90.7 12.4 6.2 600+ EE 25 17,116.0 103.4 44.3 14.0 19.9 4.3 2.2 BAU: business as usual; EE: energy-conservation and emissions-reduction. Ton/a: ton/annual.

The EE scenario considerably reduced pollutant emissions and saved more energy compared with the BAU scenario. Table4 shows that when the total installed capacity and power generation were generally constant, the coal consumption was reduced by 17.1 million tons (8.7%), and the total emissions were reduced by 79.8% (SO2), 84.9% (NOx), 60.9% (PM), and 59.5% (PM2.5). Compared with a previous study [30], the total installed capacity of thermal power units increased by 4.1%, while total emissions were reduced by 15.0% (SO2), 12.6% (NOx), and 8.4% (PM).

Table 4. Scenario comparison.

Installed Power Coal Scenario SO NO PM PM Capacity Generation Consumption 2 x 2.5 Comparison (103 ton/a) (103 ton/a) (103 ton/a) (103 ton/a) (MW) (106 MWh) (106 ton/a) BAU 54,800.5 330.1 196.4 298.4 603.2 72.0 35.1 EE 54,722.5 328.7 179.3 60.2 90.9 28.1 14.1 Ratio −0.1% −0.4% −8.7% −79.8% −84.9% −60.9% −59.9%

4. Conclusions In this study, an inventory of production scale, coal consumption, and air pollutant emissions was used to provide a reference for an energy conservation and emissions reduction strategy in the thermal power industry. Comparisons were made by clustering classification based on the installed efficiencies of energy consumption and pollutant emissions. This method can also apply to other polluting industries for the analysis of EE strategies from the perspective of emissions inventory. Our results showed that emissions from the smallest 20% of thermal power units accounted for 39.7% (SO2), 34.0% (NOx), 34.7% (PM), and 32.8% (PM2.5) of total emissions from thermal power units in the Jing-Jin-Ji Region in 2013. According to the installed efficiency, the thermal power units were divided into five classes by their installation scale: 0–50, 50–200, 200–350, 350–600, and 600–1,000 MW. The analysis of emission characteristics revealed that units <50 MW had low total installed capacity and a high ratio of pollutant emissions, and we recommend that these plants be shut down as soon as possible. The analysis of installed efficiency revealed that units >350 MW had installed efficiency of energy consumption and pollutant emissions below the average level, so we further recommend that installed capacity of new thermal power units be restricted to no less than 350 MW. Compared with BAU, the EE scenario not only considerably reduced the pollutant emissions of thermal power units, but also saved energy: when the total installed capacity and power generation Int. J. Environ. Res. Public Health 2019, 16, 938 12 of 13 were generally constant, the total coal consumption was reduced by 17.1 million tons (8.7%), whereas the total emissions were lowered by 79.8% (SO2), 84.9% (NOx), 60.9% (PM), and 59.5% (PM2.5).

Author Contributions: Conceptualization, Y.Z.; Data curation, C.Z.; Formal analysis, Y.Z. and C.Z.; Funding acquisition, Q.Z. and J.W.; Methodology, Y.Z.; Project administration, Q.Z.; Resources, J.W.; Supervision, J.W.; Visualization, Y.Z. and Q.Z.; Writing—Original draft, Y.Z. and Q.Z.; Writing—Review & editing, Y.Z. Funding: This research was funded by the National Key Technology Research and Development Program of the Ministry of Science and Technology of the People’s Republic of China, grant number 2015BAA05B02. Acknowledgments: The authors are grateful for data support provided by the Appraisal Center for Environment and Engineering Ministry of the Environmental Protection (ACEE). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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