Predicting air pollutant emissions of the foundry industry : Based on the electricity big data
Copyright © 2024. Published by Elsevier B.V..
Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:917 |
|---|---|
| Enthalten in: |
The Science of the total environment - 917(2024) vom: 20. Feb., Seite 170323 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chi, Xiangyu [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Air pollutant emissions |
|---|
| Anmerkungen: |
Date Revised 21.02.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.scitotenv.2024.170323 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM367686716 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM367686716 | ||
| 003 | DE-627 | ||
| 005 | 20240222091732.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240127s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.scitotenv.2024.170323 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1301.xml |
| 035 | |a (DE-627)NLM367686716 | ||
| 035 | |a (NLM)38278260 | ||
| 035 | |a (PII)S0048-9697(24)00458-3 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chi, Xiangyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predicting air pollutant emissions of the foundry industry |b Based on the electricity big data |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 21.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2024. Published by Elsevier B.V. | ||
| 520 | |a Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (-17.09-24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Air pollutant emissions | |
| 650 | 4 | |a Electricity data | |
| 650 | 4 | |a Relationship model | |
| 650 | 4 | |a Temporal allocation factors | |
| 700 | 1 | |a Li, Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hanqing |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jianhua |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Jian |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The Science of the total environment |d 1972 |g 917(2024) vom: 20. Feb., Seite 170323 |w (DE-627)NLM000215562 |x 1879-1026 |7 nnns |
| 773 | 1 | 8 | |g volume:917 |g year:2024 |g day:20 |g month:02 |g pages:170323 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.scitotenv.2024.170323 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 917 |j 2024 |b 20 |c 02 |h 170323 | ||