辦公室室內空氣品質之模擬分析

Abstract

辦公場所有必要保持良好的室內空氣品質(Indoor Air Quality, IAQ)以確保辦公人員健康。本研究採用二氧化碳當作室內空氣污染物指標，針對初期量測中不符合建議標準之辦公場所進行長時間監測，分析各場所二氧化碳隨時間之變化情況。然後依據各場所作業特性、人員數量、空調狀態及空間配置等特性探討室內二氧化碳超出標準的可能原因，且進一步採用CONTAM模式建立各場所的IAQ模式，以模擬辦公場所二氧化碳之濃度分佈，依據模擬結果分析各環境影響因子對CO2濃度之影響及應用CONTAM模式於各辦公場所之適用性。最後根據實測經驗、所建立的模式及模擬結果研擬室內空氣品質之改善方案。 本研究依初測及長期監測程序篩選出CO2濃度超過1,000ppm的五個重點場所作進一步分析，最高值甚至已達到1,200ppm，所建立的初測與長期監測程序亦可供其他場所診斷IAQ問題時參考使用。由各辦公場所CO2濃度監測與模擬結果之比對分析可發現，排除特殊之作業影響，密閉空間之CO2濃度影響因子較開放性空間少，其CONTAM模式之模擬結果亦因而較開放性空間良好，誤差大部分約在15-20%間。由於CONTAM模式只能模擬零維或一維之變化，故對於較開放空間模擬結果較差，誤差大部分在20%-25%間，且其中場所G4的誤差約35%。依監測及模式模擬之結果顯示，人員數量變化及空調系統操作為影響辦公場所室內CO2濃度之主要變因，此二大變因數據的完整度亦因而是影響模擬品質的主要因子。且依實測及模擬結果亦可歸納出辦公場所IAQ可以人數控制及調整空調通風換氣等方案加以改善之。

Maintaining appropriate indoor air quality (IAQ) in an office is essential to protect occupants’ health. In this study, carbon dioxide (CO2) is adopted as the primary IAQ indicator. Long-term monitoring and investigations on those indoor workplaces that had failed to pass the proposed standard in the initial stage of measurement have been conducted for analyzing IAQ temporal variation. Possible causes for excessive amount of CO2 indoors have also been explored, including operational characteristics, number of occupants, HAVC system control, and space layout. IAQ models for different indoor workplaces have been established using CONTAM to simulate the concentration distributions of CO2 under different conditions. Finally, possible solutions for improvement of IAQ have been suggested in accordance with actual measurements, established models and simulated results. After the initial and long-term monitoring, five office rooms with CO2 concentration exceeding 1,000 ppm, the maximum is about 1,200 ppm, were chosen for further analyses. The proposed initial and long-term monitoring procedure should be also applicable for diagnosing the IAQ problem at other buildings. Through comparisons and analyses of the monitored data and simulated results in different room, this study shows that the CONTAM models for closed rooms, irrespective of impact from unusual conditions, give better results than those for open rooms because the CO2 levels in closed rooms are affected by fewer factors than those in open rooms. The errors were between 15%~20% for closed rooms. While the CONTAM model could only simulate zero- or one-dimensional changes, it may produce less accurate results of simulations in open rooms. Most of the errors were between 20% and 25% for open rooms, and the error for room G4 is about 35%. According to monitoring and modeling results, temporal variations of occupants and ventilating operations are main effects for CO2 concentration in offices, and subsequently the data integrity of them can significantly affect modeling quality. Based on the results obtained in this study, solutions such as controlling the number of occupants indoors and proper operation of the HVAC system are suggested for improving the IAQ problems in these rooms.