Correlation of Building Heating and Air Qualities in Typical Cities of China

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Energy Procedia 158 Energy Procedia 00(2019) (2017)6532–6537 000–000 www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Correlation of Building Heating and Air Qualities in Typical Cities International Symposium District Heating and CorrelationThe of15th Building Heating andonAir Qualities in Cooling Typical Cities of China of China Assessing the feasibility of using the heat demand-outdoor Tingting Dua,* , Yue Sunbb a,* Tingting Du , Yuedistrict Sun temperature function for a long-term heat demand forecast School of Energy and Power Engineering, Shandong University, 17923Jingshi Road, Jinan250061, Shandong China a

a Mechanical Automation Department, The Shandong Chinese University of 17923Jingshi Hong Kong, Shatin, NT999077, Hong Kong SAR China School ofand Energy and Power Engineering, University, Road, Jinan250061, Shandong China a,b,c a a b c c b Mechanical and Automation Department, The Chinese University of Hong Kong, Shatin, NT999077, Hong Kong SAR China b

I. Andrić

a

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract Building heating causes the huge energy consumption, as well as severe environmental problems, especially the air pollution in Building causes the huge energy consumption, severe environmental especially thesoair pollution in China. Asheating the tremendous differences between the north as andwell the as south in climate, buildingproblems, type, living habits and forth in China, China. As the tremendous differences the north theair south in climate, building type, living habits so forth in mostly China, the heating system is so different thatbetween it influences the and local quality distinctively. The previous works and of literatures Abstract the heating system is so different that it the influences the in local air quality distinctively. of literatures focused on the correlation of heating and air quality a certain region, but rarely onThe the previous impact ofworks the regional feature mostly on the focused on of thethe correlation of heating quality in a certain region, but rarely onliterature the impact of the and regional feature on the correlation two subjects. Hence,and in the thisair paper, adopting household investigation, analysis, statistical analysis, District heating networks are Hence, commonly in the literature asinvestigation, one heating of the most effective solutions for decreasing the correlation of the in this paper, adopting household literature analysis, statistical analysis, the correlation andtwo thesubjects. regional difference ofaddressed the air pollution and the building were compared andand analyzed based on the greenhouse emissions fromdifference the building sector. These systems high investments which are returned through the heat the correlation and the regional of the pollution andChina therequire building heating were compared and analyzed basedfor on the data collectedgas from 604 households covering 22airprovinces in in 2016. The research can provide the evidence sales. Due totomake the changed climate conditions building policies, heat demand in the future could decrease, data collected from 604 covering 22and provinces in renovation China in 2016. The research can the evidence for the government morehouseholds rational strategies to optimize the current heating system and improve theprovide heating-season air quality. prolonging the investment return period. government to make more rational strategies to optimize the current heating system and improve the heating-season air quality. The main©scope this paper to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright 2018ofElsevier Ltd.isAll rights reserved. ©forecast. 2019 The Authors. Published Elsevier Ltd. The district of under Alvalade, located inofLisbon (Portugal), was used study. The district on is consisted of 665 Copyright © 2018 Elsevier Ltd. by All rights reserved. Selection and peer-review responsibility the scientific committee of theas10ath case International Conference Applied Energy This is an open access article under the CC period BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) buildings that vary in both construction and typology. Three weather scenarios (low, medium, high) and three district th Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – estimate The 10ththe International Conference on Applied Energy. renovation scenarios were developed (shallow, intermediate, deep). To error, obtained heat demand values were (ICAE2018). compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: building heating; air quality; correlation; regional differences The results showed that air when onlycorrelation; weather change considered, the margin of error could be acceptable for some applications Keywords: building heating; quality; regionalisdifferences (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.scenarios, Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and The global buildings sector contributes about 36% of worldwide final energy consumption and nearly 40% of total renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Theand global buildings sector contributes about 36% ofPerspectives worldwide final energy consumption andenergy nearlyconsumption 40% of total (Energycould Technology China, thefor building direct indirect CO 2 emissions coupled scenarios). The values suggested be used to modify the 2017). functionInparameters the scenarios considered, and direct and indirect CO emissions (Energy Technology Perspectives 2017). In China, the building energy consumption 2 improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd. Peer-review responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Correspondingunder author. Tel.: +86 13791033619 fax: +86 0531-88392701 * Cooling. E-mail address: [email protected]

Corresponding author. Tel.: +86 13791033619 fax: +86 0531-88392701 E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urbanand energy systems, under CUE2018. Selection peer-review responsibility of the scientific committee of the Applied Energy Symposium and Forum 2018: Low carbon cities and urban energy systems, CUE2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.105

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rapidly explodes for the acceleration of urbanization and the improvement of living standards. The number of people rushing into cities is expected to account for 60 percent of the population in the near future 2020[1], which directly results in a predictably tremendous rising in building space and in residential heating demand. Recently, building space heating has triggered concerns, not only because the heat source greatly depends on coal which is the most important fossil resource facing the danger of exhaustion, but also because it has been firmly considered as the chief culprit of air pollution for the high overlap of the occurrence between two events. Therefore, an increasing number of investigations has been sprouting out on the relationship between build space heating and air quality. Based on the Code for Thermal Design of Civil Building (GB50176-2016), the regions in severe-cold winter zone and cold winter zone are classified as geographical heating area from the perspective of meteorology. Therefore, most cities of research cases in previous literatures were located in these regions. However, owing to the abnormal fluctuation of temperature in recent years, the appeal to government is getting stronger to take the zone, where it is hot in summer and cold in winter, into the heating area. Simultaneously, as the haze zone in winter has expanded to parts of provinces in south China, wider territory of China is required to be investigated to reveal the intrinsic correlation between space heating and air pollution. It must be emphasized that there are distinct differences in climate and living habits between north and south of China, which determine the popular heating mode and the corresponding efficiency of energy usage. The heating modes, which has been discussed a lot in literatures, illustrated that district heating makes up over 70% of the building space heating in the north [2]. And individual heating via coal-burning methods occupied the remainder for a long period, which aggravated the haze. In the south, the major mode is to adopt household heating appliance including electric blanket, electric radiator, and air conditioner. Although these appliances rarely exhaust polluted gas and particles, the primary-energy conversion efficiency is relatively low as amount of energy is consumed in the transport process. On account of the increasing frequency of the haze occurring in the south, it is necessary to explore the influence of current heating modes on environment, which hasn’t been yet illustrated in much more researches. In view of the research status, this investigation focused on the correlation between heating mode and air pollution in typical cities located in both north and south of China. The north-south differences in heating modes, indoor temperature, and thermal comfort were taken as the influence factors related with the air pollution. The conclusion can offer evidence to government to acquire the reason for the haze occurring and to make relevant policy. 2. Material and methods 1.1. Data collection The survey was administrated by the School of Energy and Power Engineering at Shandong University of China during January and February in 2016, covering 22 provinces and 604 urban households randomly selected. Various building types, such as low block, apartment, and villa were involved. Herein, users’ data were based on the household survey and questionnaire. Meteorological data and air quality data were collected by National Bureau of Statistics of the People’s Republic of China and Ministry of Environmental Protection of the People’s Republic of China. 2.2 Data analysis methods Data analysis were performed using SPSS 20.0. The heating season of case cities was identified by 5-day moving average method in meteorology to eliminate abnormal temperature fluctuation. The correlation between space heating and air quality was analysed by Spearman. 3. Results and discussion 3.1. Regional difference analysis on space heating Base on the Code for Thermal Design of Civil Building (GB50176-2016), the cities in this survey are categorized into different thermal design zones, shown in Table. 1. In order to express the zones briefly, the full names of severe cold zone, cold zone, hot-summer and cold-winter zone and hot-summer and warm-winter zone are written as S zone, C zone, H-C zone and H-W zone for short.

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Zone

Distrib ution

No. 1 2 3 4 5 6 7

Table. 1 Provincial distribution of cities in the survey S zone C zone H-C zone Province No. Province No. Province Jilin 8 Tianjin 14 Sichuan Gansu 9 Shandong 15 Jiangsu Liaoning 10 Shanxi 16 Jiangxi Heilongjiang 11 Shaanxi 17 Anhui Xinjiang 12 Henan 18 Hunan Inner Mongolia 13 Hebei 19 Hubei Qinghai 20 Fujian 21 Zhejiang

3

No. 22

H-W zone Province Guangdong

The heating modes in four zones are illustrated in Fig. 1. It shows that district heating is the major heating mode in both S zone and C zone. District heating by finned radiator and radiant floor has accounted for 88.24% in the S zone, and the proportion is 67.91% in C zone, respectively. Besides, heating appliance also takes up a certain percentage in these regions, especially in C zone where its scale is nearly a quarter. The survey also presents the large demand for heating in the other zones. The scale of no heating all year round is only 11.82% in H-C zone and 26.32% in H-W zone. The main reason for that is due to the climate feature of high humidity and less sunshine in winter, which usually results in the low body temperature. Without district heating, residents prefer heating appliance on account of low cost in installation and operation. In H-C zone, the scale of this heating mode is about 70%, which is much larger compared to a half of the proportion in H-W zone. In addition, although there is a small scale of district heating, the usual heating supply is not from the government, and the heating area is considerably smaller than that in S zone and C zone. Meanwhile, the survey shows the proportion of coal-burning heating is still on a certain scale as the individual heating mode in C zone and in H-C zone. Solar heating mode hasn’t been a popular choice for urban residents because the initial invest is relatively high and it is affected by the weather a lot. This mode is obviously more adopted in HW zone than in the rest zones as the longer duration of sunshine. Except these heating modes listed in the survey, other modes are not further listed by participates. That means the survey has been covered the most current heat mode of urban residents. 100

Percentage (%)

80 A B C D E F G

60 40 20 0

S zone

C zone

H-C zone

H-W zone

Zone

Heating appliance (air conditioner, electric radiator, etc.) floor, etc.)

District heating (water boiler, finned radiator, etc.)

Solar heating Coal-burning heating (household stove, etc.) Others Fig. 1 Heating modes and proportion in the four zones

District heating (radiant

No heating

3.2 Indoor temperature and thermal comfort Based on the household data, the average indoor temperature in S zone is 19.89C, which is about 5C higher than that in C zone. The average indoor temperature in H-C zone and H-W zone is respectively 12.02C and 15.97C that is close to average outdoor temperature. Fig. 2 shows the indoor temperature range of cases in the survey. In S zone

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with the province label from 1 to 7, the highest indoor temperature can reach 28C，but there are household temperature in several provinces lower than the average. The indoor temperature wave range in C zone with the label from 8 to 13 is relatively larger, and a great deal of household temperature is above the average value. In the rest zones, it is found that the range of the indoor temperature below the average value is wider, that is to say, a proportion of household temperature can’t reach the average temperature. The lowest temperature in some provinces is below zero, which reduces the thermal comfort of body temperature. Thermal comfort is a kind of human-body experience that express the satisfaction with thermal environment. In China, according to Code for Design of Heating Ventilation and Air Conditioning (GB50736-2012), the indoor temperature in S zone and in C zone is from 18C to 24C, and from 16C to 22C in H-C zone. By the terms, the indoor temperature is classified into over-temperature, proper-temperature and hypo-temperature ranges, and the ratio of each range is displayed in Fig. 3. There is a distinct difference between north and south of China. The indoor temperature of most urban cities in the north is proper, especially in C zone where the percentage of propertemperature range accounts for 52.38%. However, over-temperature phenomenon is the serious problem in these two zones, with nearly the same ratio of 22.5% and 22.2%. And there is still a part of residents living below the thermalcomfort temperature. Comparatively, hypo-temperature ratio is extremely high in the south. In the H-C zone, the proportion is approximately 58.3% and over-temperature ratio is very low. The hypo-temperature range accounts for 53.3% in HW zone. Therefore, the urge problem is to solve the reasonable supply and uniform heat distribution. 35

Below average Above average

100

25

80

Percentage (%)

Indoor

temperature range (C)

30

20 15 10 5

40

20

0 -5

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Provinces

Fig. 2 Indoor temperature range in case provinces

0

S zone

C zone

H-C zone

H-W zone

Zone

Over-temperature range Proper-temperature range Hypo-temperature range Fig. 3 Ratio of indoor temperature range in the four zones

3.3 Air quality in heating season and the correlation with building space heating For the purpose of analysis on the correlation between building space heating and air quality, there are nine typical cities chosen from the provinces to investigate. According to GB50736-2012, when the yearly average temperature is lower than or equals to the critical outdoor temperature which is usually set as 5C, the beginning day of heating season can be identified. Five-day moving average method in meteorology was the common method used to identify the beginning day as it can eliminate abnormal temperature fluctuation to some degree. Table. 2 listed the information of heating season in 2015-2016, as well as the days of various air quality during the space heating period. It shows that the spacing heating date generally starts from the late October or the early November, and ends in March. The polluted days in north China are much more than air-good days in this period. On the contrary, the space heating period is relatively short in the south where there is no distinct gap between the airpolluted days and the air-good days. The daily Air Quality Index of the typical cities during the space heating period is illustrated in Fig. 4. The air pollution was considerably severe for the high index of air quality. Correspondingly, the air-polluted days covered the most space heating period. By contrast, the air quality in the south cities is better with lower air quality index. And the correlation between space heating and air pollution seems not to be obvious. The reason for that is probably due

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to the living habit of individual heating mode. But the occurrence of air pollution in the south must be paid more attention to further prevention and management. Table. 2 Heating season and air quality information in typical cities Total Days of various air quality Province Beginning date Ending date heating Good Polluted days Liaoning Nov 11, 2015 Mar 26, 2016 137 71 66 Heilongjiang Nov 5, 2015 Mar 26, 2016 143 66 77 Xinjiang Oct 23, 2015 Mar 22, 2016 152 38 114 Inner Mongolia Oct 26, 2015 Mar 27, 2016 154 83 71 Shandong Nov 22, 2015 Mar 11,2016 111 19 92 Sichuan Jan 20, 2016 Feb 4, 2016 16 10 6 Jiangsu Jan 7, 2016 Feb 8, 2016 33 12 11 Hubei Jan 7, 2016 Feb 5, 2016 30 8 22 Zhejiang Jan 18, 2016 Feb 7, 2016 21 14 7 500

Space heating day Air-polluted day Air-good day

400

Air Quality Index of Ha'erbin

Air Quality Index of Shenyang

500

300

200

100

0

2015-12-20

2016-02-08

400

300

200

100

0

2016-03-29

Date

300

200

100

2016-01-08

300

200

100

0

2016-02-27

2015-12-20

2016-02-08

2015-11-29

300

200

100

2016-02-09

2016-02-29

500

2016-03-28

Space heating day Air-polluted day Air-good day

400

300

200

100

0

2016-03-29

Space heating day Air-polluted day Air-good day

400

300

200

100

0

2016-01-28

2016-01-20

Date

2016-02-09

2016-02-29

500

500

Air Quality Index of Wuhan

Air Quality Index of Nanjing

100

Date

Space heating day Air-polluted day Air-good day

Date

200

Date

400

500

2016-01-20

Space heating day Air-polluted day Air-good day

300

0

Space heating day Air-polluted day Air-good day

Date

0

400

2016-03-29

Air Quality Index of Chengdu

Air Quality Index of Jinan

Air Quality Index of Hohhot

400

400

2016-02-08

500

Space heating day Air-polluted day Air-good day

2015-11-19

2015-12-20

500

Date

500

0

Space heating day Air-polluted day Air-good day

Air Quality Index of Urumchi

Shenyang Ha’erbin Urumchi Hohhot Jinan Chengdu Nanjing Wuhan Hangzhou

Air Quality Index of Hangzhou

Typical cities

300

200

100

0

2016-01-20

2016-02-09

Space heating day Air-polluted day Air-good day

400

2016-02-29

Date

2016-01-20

2016-02-09

2016-02-29

Date

Fig. 4 Daily AQI of the typical cities during the space heating period

3.4 Correlation analysis on space heating and air quality Taking population scope, GDP, energy consumption and space heating intensity as the criteria, the correlation between space heating and air quality of the typical cities was analysed by Spearman. The results are shown in Table. 3. It is concluded that the air quality is positively correlated with district heating quantity and the period of space heating. And it has a negative correlation with GDP and housing areas. The energy consumption is closely influenced by population scope, housing areas and heating areas. In S zone and C zone, the over-temperature heating

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mode can stimulate the energy consumption and induce more air-polluted possibility. Though the population has been continuously increase, it seems there is no distinct correlation between them.

Population

Table. 3 Correlation of air quality and related factors Energy Total housing Total district consumption GDP areas heating quantity per GDP

District heating areas

Population

1

-0.40

0.55

0.75*†

-0.27

-0.36

Energy consumption per GDP

-0.40

1

-0.80**

-0.62

0.22

0.09

GDP

0.55

-0.80**‡

1

0.92**

-0.66

-0.59

0.75*

-0.62

0.92**

1

-0.63

-0.61

-0.27

0.22

-0.66

-0.63

1

0.91**

-0.36

0.09

-0.59

-0.61

0.91**

1

Total housing areas Total district heating quantity District heating areas

4. Conclusions The correlation between space heating and air quality in north and south of China was investigated and analyzed. The results demonstrated that the various heating modes in different regions determine the indoor temperature and thermal comfort. The uncontrollable-temperature equipment and the imbalanced heat distribution are the primary issues for both energy consumption and residential dissatisfaction. In S zone and C zone, mainly covering the north of China, the air quality is positively correlated with the heating mode and heating quantity. Currently, district heating is still the optimal mode for urban cities. And the increasing occurrence of air pollution in the south should be paid more attention. As the requirement for heating in the south is getting stronger, it is necessary to consider the current heating mode. Acknowledgements This work was kindly supported by National Basic Research Program of China (973 Program) (2013CB228305) and International Clean Energy Talent Program by China Scholarship Council (No. 201802180014). References [1] Xinye Zheng, Chu Wei, Ping Qin, Jin Guo, Yihua Yu, Feng Song, Zhanming Chen, Characteristics of residential energy consumption in China: Findings from a household survey, Energy Policy, 75(2014):126-135. [2] Yi Jiang, Establishment and implementation of the Code for Thermal Design of Civil Building, Construction Science and Technology, 02(2017):1671-3915.

* The correlation between the decrease of air quality during the heating period was significant at 0.05 level (double tail) **The correlation between the decrease of air quality during the heating period was significant at 0.01 level (double tail)