# Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow

^{1}

^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

## 2. Analysis of Influencing Factors of Carbon Emissions in Large-Scale Comprehensive Medical Facilities

#### 2.1. Definition of Large-Scale Comprehensive Medical Facilities

^{2}; therefore, the flow density should not be greater than 0.25 person/m

^{2}. However, the actual flow density of large-scale comprehensive medical facilities can reach 1.3 person/m

^{2}, which exceeds five times the design flow threshold [16,17].

#### 2.2. Grounded Theory Analysis

_{2}. In order to study the factors related to the flow of people, the bibliometric visualization analysis is carried out through the correlation degree and hot spot distribution.

#### 2.3. Visualization Analysis of Literature Based on VOSviewer

_{2}. Different population bases affect the changes in the indoor environment because of the strict constraints of the indoor environmental indicators of medical facilities, increasing the operating load of equipment and additional carbon emissions.

## 3. Experimental Analysis

#### 3.1. Purpose of the Experiment

#### 3.2. Experimental Organization Design

#### 3.2.1. Experimental Theoretical Basis

^{2}. The growth is irregular when the flow density exceeds 0.41 p/m

^{2}. In order to study the law clearly, the analysis is focused on the (0, 0.4).

_{2}concentration was designed. The corresponding relationships were deduced according to the measured data, which provided the basic data for model construction.

#### 3.2.2. Experimental Scheme

^{2}, covering six different human flow densities between 0 and 0.4 people/m

^{2}. We designed five groups of equal numbers of people and four groups of equal numbers of people, i.e., a 1, 2, 3, 4, and 5 people array and a 1, 2, 4, and 8 people array. In order to avoid the test error caused by outdoor temperature, humidity, enclosure structure, and other factors, a 240 min continuous test was carried out at different time periods to compare the operation cycle of the medical facilities. The arithmetic array was measured from 8:00 to 12:00, and the geometric array was measured from 13:30 to 17:30. To avoid extreme error values, each group was repeated three times, and a mean value analysis was performed.

_{2}sensor was performed, as shown in Figure 6. The upload time interval was 10 min, and the indoor CO

_{2}concentration overrun benchmark was set to 1000 ppm.

#### 3.3. Experimental Process

^{2}and 25 m

^{2}were selected for 4 h of continuous control test. In order to prevent thermal disturbance caused by outdoor temperature differences, according to the requirements of the Technical Specifications for Monitoring of Indoor Air Quality [31], the sensor probes of each module were placed at 50 cm away from the outer wall side and uniformly installed, as shown in Figure 7a,b. In the three repeated tests, the sensor probes of each module were exchanged, and the indoor wind speed measuring device was placed at the ventilation position.

_{2}sensor has a built-in probe, which was connected to the 12 V power supply interface through the adapter, and the power supply and communication terminals were connected to 10 A and 250 V wiring, as shown in Figure 8c. After adjusting the secondary parameters of the digital display meter of the wind speed measurement terminal, the indoor ventilation volume was measured, as shown in Figure 8d.

#### 3.4. Experimental Analysis

_{2}concentration and negatively correlated with humidity. A quantitative analysis of human flow density and environmental measurement increments was conducted every 30 min to analyze further the mechanism of human flow in indoor environment measurements.

#### 3.4.1. Temperature Increment Analysis Based on People Flow Density

_{j}is the coefficient of the x term of class j; ${R}_{i,j}^{2}$ is the goodness of fit of the relationship in i period; a

_{i,j}is the j-coefficient of x in i-period i. Using Equation (1), the relationship between the flow density and indoor temperature increment was calculated, as shown in Equation (2):

_{v}is the temperature increment (°C) and x is the population flow density (people/m

^{2}).

#### 3.4.2. Humidity Increment Analysis Based on Population Flow Density

_{v}is the humidity increment (%) and x is the population flow density (people/m

^{2}).

#### 3.4.3. Incremental Analysis of CO_{2} Concentration Based on Population Flow Density

_{2}concentration increment was analyzed every 30 min, as shown in Table 7. The trend of the relationship between the test time and the increment in the CO

_{2}concentration under different person arrays is shown in Figure 12a, and the trend of the relationship between the flow density and the increment in the CO

_{2}concentration under different time periods is shown in Figure 12b.

_{2}concentration and the number of personnel during the same period. Quantitative analysis of the flow density and increment in CO

_{2}concentration was performed at eight equidistant time points. Figure 12b displays a consistent trend between the increase in CO

_{2}concentration and flow density at different time periods. Curve fitting of the CO

_{2}concentration increment and human flow density was performed, the coefficient was weighted, and the relationship between the two was obtained, as shown in Equation (4):

_{c}is the CO

_{2}concentration increment (ppm) and x is the population flow density (people/m

^{2}). The population produced a certain amount of CO

_{2}emissions, as measured by the test. The increase in CO

_{2}concentration was related to the volume of indoor space, i.e., 1 ppm = (M ÷ 22.4) mg/m

^{3}, and M is the molecular weight of CO

_{2}. For every 1 ppm increase in CO

_{2}concentration measured in the test, 37.63 × 10

^{−3}kg CO

_{2}increased correspondingly. The direct emission of CO

_{2}measured during the tests is presented in Figure 13.

_{2}emissions in different test groups, the direct emission of indoor CO

_{2}was 0.301 kgCO

_{2}/(p·h). The relationship between the density of people and the direct emission of CO

_{2}is shown in Equation (5):

_{w}is the CO

_{2}direct emission increment (kgCO

_{2}); S is the area of population flow (m

^{2}); A is the personnel residence time (h); and x is the population flow density (people/m

^{2}).

_{2}directly emitted by the flow of people and the CO

_{2}indirectly emitted by the flow of people affecting the operating load of the equipment which together constituted the carbon emissions caused by a change in the flow of people.

## 4. Accounting Model Construction and Verification

_{2}into the surroundings, causing the actual performance of the building to deviate significantly from the design conditions. To ensure the control effect of various systems and realize energy-saving operations, the carbon emission boundary must be clarified based on the existing carbon emission measurement framework and construct a carbon emission accounting model for large-scale comprehensive medical facilities based on changes in population flow [29].

#### 4.1. Framework of Accounting Model

#### 4.1.1. Accounting Basis

_{M}is the carbon emission per unit building area in the building operation stage; A is the building area; E

_{i,j}is the i-th energy consumption of the j-class system; ER

_{i,j}is the class j system’s consumption of the class i energy provided by the renewable energy system; EF

_{i}is the carbon emission factor of class i energy; C

_{p}is the annual carbon reduction in the building’s green space carbon sink system; y is the design life of the building; i is the building’s energy consumption terminal type, including electricity, gas, oil, and municipal heating; and j is the type of building energy system, including heating, air conditioning, lighting, domestic hot water, and other systems. Because the floating energy system in this study is an air-conditioning HVAC system and fresh air system, the parameter m in Equation (6) is analyzed by 2.

#### 4.1.2. Environmental Constraints

_{2}≤ 1000 ppm; according to the different functional areas of the hospital, the temperature should be between 18 and 22 °C or 22 and 24 °C; the temperature constraint condition is set according to the area proportion of different regions; humidity should be in the range of 50–60% [33].

#### 4.2. Construction of Accounting Model

_{2}concentration were caused by the flow of people, as shown in Equations (2)–(4). Equation (5) expressed the direct emission of CO

_{2}caused by a crowd.

_{2}/m

^{2}according to the existing research [33], so the carbon neutral value of the carbon sink system within the geographical boundary of the building can be estimated according to the green area. The amount of carbon sequestration per unit of green area needs to be measured according to different types of vegetation, and this study does not make uniform provisions.

#### 4.2.1. Carbon Emission Model of Air-Conditioning System Based on People Flow

_{a}in different regions, and the heat transfer coefficient φ of different envelope structures was taken to obtain the temperature starting value φT

_{a}. According to the variation law of temperature increment T

_{v}obtained from the test and the temperature constraint value T

_{f}in the medical facility, the temperature overrun part was obtained, i.e., $\Delta T=\phi {T}_{a}+{T}_{v}-{T}_{f}$. Equation (7) is obtained by substituting the personnel density into the equation:

_{a}is the seasonal outdoor average temperature (°C); T

_{f}is the upper limit of indoor temperature constraint (°C); and x is the population flow density (people/m

^{2}).

^{3}); C

_{t}is the carbon emissions of air-conditioning temperature control system (kgCO

_{2}); ρ is the average density of air (kg/m

^{3}); r

_{t}is the cooling capacity of air-conditioning equipment (W); the air specific heat is 1008 J/kg⋅°C; P

_{i}is the power of equipment using category i energy; and EF

_{i}is the carbon emission factor of the i-th energy.

_{v}obtained from the experiment and the corresponding humidity constraint value H

_{f}were used to obtain the required dehumidification amount in the environment $\Delta H={H}_{a}+{H}_{v}-{H}_{f}$ and the personnel density was substituted to obtain Equation (10):

_{a}is the seasonal outdoor relative humidity (%); H

_{f}is the restricted upper limit of indoor relative humidity (%); x is the population flow density (people/m

^{2}).

_{h}is the carbon emission of the air-conditioning wet system (kgCO

_{2}); V is the interior space (m

^{3}); 1.2 is the assurance coefficient; ρ is the average density of air (kg/m

^{3}); EF

_{i}is the carbon emission factor of the i-th energy; and P

_{w}is the amount of dehumidification per unit input power (kg/kW∙h).

#### 4.2.2. Carbon Emission Model of Fresh Air System Based on People Flow

_{2}in the atmosphere was 400 ppm. According to the CO

_{2}concentration increment change C

_{c}and the corresponding concentration constraint value C

_{f}obtained from the experiment, the transfinite part of the concentration $\Delta C=400+{C}_{c}-{C}_{f}$ can be obtained, and the calculation in Equation (12) can be combined as follows:

_{2}concentration; C

_{f}is the upper limit of indoor CO

_{2}concentration constraint; and x is the people flow density (people/m

^{2}).

_{2}concentration exceeding the limit is calculated. The power consumption per unit air volume is given by Equation (13):

_{s}is the power consumption per unit air volume (kW∙h/m

^{3}); P is the fan wind pressure of the fresh air system (Pa); η

_{CD}is the motor and transmission efficiency (take 0.855); and η

_{F}is the fan efficiency (%).

_{2}concentration environment in a central fresh air system is shown in Equation (14):

_{2}); V is the interior space (m

^{3}); EF

_{i}is the carbon emission factor of the i-th energy; and the gaseous density of CO

_{2}is 19.77 mg/L.

#### 4.2.3. Carbon Emission Model Based on People Flow

^{2}, the indoor CO

_{2}concentration exceeded the upper limit threshold, and the energy consumption of the fresh air system was generated during the start-up operation. The survey showed that the inflection point of hospital energy consumption increment (Figure 5, x = 0.17) was near x = 0.2, the model was constructed in the interval of (0, 0.2), (0.4, +∞).

_{i}is the increment in carbon emission of type i equipment system caused by the flow of people (kgCO

_{2}); C

_{t}is the carbon emissions of the air-conditioning temperature control system (kgCO

_{2}); C

_{h}is the carbon emission of the air-conditioning wet system (kgCO

_{2}); F is the carbon emissions of the central fresh air system caused by the flow of people (kgCO

_{2}); V is the interior space (m

^{3}); and EF

_{i}is the carbon emission factor of the i-th energy

^{−3}/60852ηF.

_{2}produced by the population itself, which was obtained from Equation (5). The direct and indirect increments were substituted into the accounting framework to obtain the carbon emissions accounting model caused by the flow of people, as shown in Equation (16):

_{2}); C

_{i}is the increment in carbon emissions of type I equipment system caused by the flow of people (kgCO

_{2}); θ

_{i,j}is the type i equipment using the correction coefficient of type j energy (determined by the energy consumption category and energy consumption ratio of equipment operation); C

_{w}is the CO

_{2}direct emission increment (kgCO

_{2}); and y is the design life of the building.

_{1,e}ϵ (0.36, 0.46), θ

_{2,e}ϵ (0.03, 0.04) [37], as shown in Equation (17):

_{2}); x is the population flow density (people/m

^{2}); y is the design life of the building; a is the coefficient, take (10V∙EF

_{i}, 70V∙EF

_{i}); b is the coefficient, take (0.16V∙EF

_{i}, 0.5V∙EF

_{i}); c is the coefficient, take (0.3S–0.001V∙EF

_{i}, 0.6S–0.002V∙EF

_{i}); d is the coefficient, take (0.001V∙EF

_{i}, 0.002V∙EF

_{i}); e is the coefficient, take (0.002V∙EF

_{i}, 0.003V∙EF

_{i}).

#### 4.3. Verification of Accounting Model

^{4}kW∙h. The carbon emission factor of the Sichuan power grid is 0.5257 kgCO

_{2}/kW∙h [38], and the actual total carbon emission caused by the average annual power consumption is 2.042 × 10 tCO

_{2}.

^{2}. Therefore, the flow density in the medical facility was calculated to be 0.237 people/m

^{2}because the out-patient and emergency flow area accounted for 6–20% of the total area (approximately 30,000 m

^{2}) [18]. The average annual temperature in Sichuan Province is 16.8 °C, and the average humidity is 74% [20]. After being substituted into the model, the floating carbon emissions accounting value based on human flow was 2.106 × 10

^{4}tCO

_{2}, and the error with the actual value was 3.07%, less than 5%. Therefore, the accounting value of the model interacts well with the actual value. The CO

_{2}emissions directly generated by the flow of people were not proposed in the statistical report. According to the model calculation, the annual carbon emission statistics of the hospital had an error of 646.703 tCO

_{2}. Therefore, this model is based on the flow of people. The error in the accounting results is low, and the dynamic accounting model is optimized by integrating the indirect effects of the flow of people and equipment.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Annual footfall data for medical facilities by region in China [20].

**Figure 3.**Theoretical model of carbon emission influencing factors for large-scale integrated medical facilities.

**Figure 8.**(

**a**). Temperature sensor; (

**b**). humidity sensor; (

**c**). CO

_{2}sensor; (

**d**). wind speed sensor.

**Figure 9.**(

**a**). Temperature variations for different groups of people; (

**b**). humidity variations for different groups of people; (

**c**). the change in CO

_{2}concentration in different groups of people.

**Figure 10.**(

**a**). Variation in temperature increments for different groups of people; (

**b**). temperature increments of different flow density.

**Figure 11.**(

**a**). Variation in humidity increments for different groups of people; (

**b**). humidity increments of different flow density.

**Figure 12.**(

**a**). The incremental change in CO

_{2}concentration in different arrays of people; (

**b**). CO

_{2}concentration increment in different human flow densities.

Source of Original Materials | Open Coding | ||
---|---|---|---|

Phenomenon Summary | Conceptualization | Categorization | |

China Energy News | a1 The functional orientation of the building is diverse | aa1 Scale of medical facilities (a1, a7) | A1 Overall layout of the medical area (aa1) |

a2 There is a great deal of equipment and facilities | aa2 The density of the system terminal (a2, a6) | A2 Equipment density (aa2) | |

a3 Large flow of people | |||

a4 Types of energy supply | aa3 Personnel density (a3) | A3 Population flow (aa3) | |

a5 Complex energy system | |||

a6 More energy-consuming equipment | aa4 Energy consumption monitoring system (a4, a5) | A4 Energy of formation (aa4, aa5) | |

a7 The continuity requirements of energy system are high. | aa5 Energy management (a8) | ||

a8 Extensive manual management | |||

… | … | … |

Item | Links | Total Link Strength | Occurrences | Recurrence Frequency |
---|---|---|---|---|

carbon footprint | 171 | 1947 | 108 | 24.36% |

patients | 302 | 6283 | 265 | 56.78% |

indoor air quality | 135 | 1641 | 58 | 3.6% |

indoor temperature | 152 | 825 | 56 | 15.68% |

… | … | … | … | … |

**Table 3.**Correlation analysis table of density of people and consumption of power natural gas [24].

The Density of People | Power Consumption | Natural Gas Consumption | |
---|---|---|---|

The density of people | 1 | 0.731 ** | 0.706 ** |

Power consumption | 0.731 ** | 1 | 0.123 |

Natural gas consumption | 0.706 ** | 0.123 | 1 |

H_{i} | DP (p/m^{2}) | BV (10^{4} kW∙h) | AV (10^{4} kW∙h) | H_{i} | DP (p/m^{2}) | BV (10^{4} kW∙h) | AV (10^{4} kW∙h) |
---|---|---|---|---|---|---|---|

H_{1} | 0.066 | 578.16 | 651.067 | H_{13} | 0.323 | 3296.2 | 3722.2 |

H_{2} | 0.085 | 868.7 | 874.833 | H_{14} | 0.35 | 3579.5 | 3963.6 |

H_{3} | 0.137 | 1400 | 1547.91 | H_{15} | 0.381 | 3898.9 | 4286.7 |

H_{4} | 0.1529 | 1562.7 | 1790 | H_{16} | 0.407 | 4158 | 3104.7 |

H_{5} | 0.1813 | 1852.9 | 1872 | H_{17} | 0.411 | 4200 | 2834.9 |

H_{6} | 0.1933 | 1975.5 | 2120 | H_{18} | 0.43 | 4396 | 2953.1 |

H_{7} | 0.2111 | 2157.4 | 1571 | H_{19} | 0.431 | 4406.5 | 4742.5 |

H_{8} | 0.2187 | 2235.1 | 2294 | H_{20} | 0.449 | 4588.8 | 2991 |

H_{9} | 0.22 | 2246.4 | 2491 | H_{21} | 0.462 | 4721.6 | 3097.6 |

H_{10} | 0.2339 | 2390.5 | 2764 | H_{22} | 0.465 | 4752.3 | 2999.7 |

H_{11} | 0.278 | 2840.2 | 3192.2 | H_{23} | 0.479 | 4900 | 5000.91 |

H_{12} | 0.298 | 3043.7 | 3422.1 |

_{i}: hospital i; DP: the density of people; BV: the base value of electric energy consumption; AV: actual value of electricity consumption.

0 min | 30 min | 60 min | 90 min | 120 min | 150 min | 180 min | 210 min | 240 min | |
---|---|---|---|---|---|---|---|---|---|

1P | 0 | 0.3 | 0.9 | 1.4 | 1.7 | 2 | 2.6 | 3.3 | 3.7 |

2P | 0 | 0.5 | 1.5 | 1.8 | 2.7 | 2.9 | 3.1 | 3.4 | 3.9 |

3P | 0 | 0.5 | 1.6 | 2.2 | 2.8 | 3.7 | 4.0 | 4.6 | 4.9 |

4P | 0 | 0.6 | 1.7 | 2.2 | 3.7 | 4.2 | 4.8 | 5.6 | 6.1 |

5P | 0 | 0.6 | 1.3 | 2.0 | 2.7 | 3.8 | 5.0 | 5.7 | 6.2 |

8P | 0 | 0.8 | 1.9 | 2.5 | 3.7 | 4.7 | 5.4 | 6.1 | 7.2 |

0 min | 30 min | 60 min | 90 min | 120 min | 150 min | 180 min | 210 min | 240 min | |
---|---|---|---|---|---|---|---|---|---|

1P | 0 | 0.2 | 0 | −0.3 | −0.3 | −0.5 | −0.6 | −1.4 | −1.5 |

2P | 0 | 0.6 | 0.3 | −0.4 | −1.0 | −1.5 | −1.7 | −2.6 | −3.0 |

3P | 0 | −0.2 | −0.5 | −0.5 | −1.3 | −1.4 | −1.9 | −2.9 | −4.6 |

4P | 0 | −0.3 | −1.0 | −1.4 | −2.3 | −1.7 | −2.6 | −4.2 | −5.1 |

5P | 0 | −0.6 | −1.8 | −2.0 | −2.6 | −3.5 | −4.4 | −5.4 | −7.5 |

8P | 0 | −0.8 | −0.9 | −1.9 | −2.6 | −3.8 | −4.8 | −6.9 | −9.5 |

0 min | 30 min | 60 min | 90 min | 120 min | 150 min | 180 min | 210 min | 240 min | |
---|---|---|---|---|---|---|---|---|---|

1P | 0 | 27 | 57 | 78 | 91 | 103 | 115 | 144 | 112 |

2P | 0 | 67 | 114 | 107 | 104 | 116 | 146 | 187 | 227 |

3P | 0 | 80 | 119 | 174 | 212 | 258 | 308 | 340 | 402 |

4P | 0 | 86 | 127 | 189 | 283 | 316 | 379 | 418 | 447 |

5P | 0 | 96 | 166 | 242 | 302 | 373 | 391 | 502 | 497 |

8P | 0 | 184 | 343 | 452 | 525 | 530 | 515 | 510 | 570 |

Environmental Requirements Indicators | GB 37488-2019 | GB/T 18883-2022 | GB/T 51366-2019 | GB 50333-2013 |
---|---|---|---|---|

concentration of CO_{2} | ≤0.1% | ≤0.1% | ≤0.1% | / |

requirement of fresh air | ≥20 m^{3}/(h·p) | / | ≥10 m^{3}/(h·p) | / |

indoor air velocity | ≤0.3 m/s | ≤0.5 m/s | ≤0.2 m/s | / |

humidity of design | 40–65% | / | 55% | ≤60% |

temperature of design | 16–20 °C(w)/26–28 °C(s) | 20 °C(w)/26 °C(s) | 21–25 °C |

Year | Electric Power (10^{4} kW∙h) | Number of Visits (10^{4} Person) | Medical Area (10^{4} m^{2}) |
---|---|---|---|

2005 | 3192.2 | 202.87 | 23.82 |

2006 | 3422.1 | 217.41 | 29.99 |

2007 | 3722.2 | 235.44 | 33.67 |

2008 | 3936.6 | 255.68 | 34.91 |

2009 | 4286.7 | 278.49 | 39.40 |

2010 | 4742.5 | 314.75 | 41.73 |

Mean Value | 3883.72 | 250.77 | 33.92 |

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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Yan, X.; Luo, Q.; Chen, Z.; Yan, Y.; Qiu, T.; Cheng, P.
Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow. *Buildings* **2024**, *14*, 748.
https://doi.org/10.3390/buildings14030748

**AMA Style**

Yan X, Luo Q, Chen Z, Yan Y, Qiu T, Cheng P.
Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow. *Buildings*. 2024; 14(3):748.
https://doi.org/10.3390/buildings14030748

**Chicago/Turabian Style**

Yan, Xikang, Qinyu Luo, Zeyu Chen, Yunhan Yan, Tian Qiu, and Peng Cheng.
2024. "Carbon Emission Accounting Model for Comprehensive Medical Facilities Based on Population Flow" *Buildings* 14, no. 3: 748.
https://doi.org/10.3390/buildings14030748