Validation of the Automatic Real-Time Monitoring of Airborne Pollens in China Against the Reference Hirst-Type Trap Method
Yiwei Liu,
Wen Shao,
Xiaolan Lei,
Wenpu Shao,
Zhongshan Gao,
Jin Sun,
Sixu Yang,
Yunfei Cai,
Zhen Ding,
Na Sun,
Songqiang Gu,
Li Peng,
Zhuohui Zhao
Affiliations
Yiwei Liu
NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
Wen Shao
Shanghai Chenshan Botanical Garden, Shanghai 201602, China
Xiaolan Lei
NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
Wenpu Shao
NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
Zhongshan Gao
Institute of Immunology and Allergy Research Center, School of Medicine, Zhejiang University, Hangzhou 310058, China
Jin Sun
NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
Sixu Yang
Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China
Yunfei Cai
Department of General Management and Statistics, Shanghai Environment Monitoring Center, Shanghai 200235, China
Zhen Ding
Department of Environment and Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
Na Sun
Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China
Songqiang Gu
Shanghai Pudong New Area Meteorological Bureau, Shanghai 200135, China
Li Peng
Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Service, Shanghai 200030, China
Zhuohui Zhao
NHC Key Laboratory of Health Technology Assessment, Key Laboratory of Public Health Safety of the Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China
Background: There is a lack of automatic real-time monitoring of airborne pollens in China and no validation study has been performed. Methods: Two-year continuous automatic real-time pollen monitoring (n = 437) was completed in 2023 (3 April–31 December) and 2024 (1 April–30 November) in Shanghai, China, in parallel with the standard daily pollen sampling(n = 437) using a volumetric Hirst sampler (Hirst-type trap, according to the European standard). Daily ambient particulate matter and meteorological factors were collected simultaneously. Results: Across 2023 and 2024, the daily mean pollen concentration was 7 ± 9 (mean ± standard deviation (SD)) grains/m3 by automatic monitoring and 8 ± 10 grains/m3 by the standard Hirst-type method, respectively. The spring season had higher daily pollen levels by both methods (11 ± 14 grains/m3 and 12 ± 15 grains/m3) and the daily maximum reached 106 grains/m3 and 100 grains/m3, respectively. A strong correlation was observed between the two methods by either Pearson (coefficient 0.87, p p 2 = 0.76) and multiple linear regression models (R2 = 0.76) showed a relatively high goodness of fit, which remained robust using a 5-fold cross-validation approach. The multiple regression mode adjusted for five additional covariates: daily mean temperature, relative humidity, wind speed, precipitation, and PM10. In the subset of samples with daily pollen concentration ≥ 10 grains/m3 (n = 98) and in the spring season (n = 145), the simple linear models remained robust and performed even better (R2 = 0.71 and 0.83). Conclusions: This is the first validation study on automatic real-time pollen monitoring by volumetric concentrations in China against the international standard manual method. A reliable and feasible simple linear regression model was determined to be adequate, and days with higher pollen levels (≥10 grains/m3) and in the spring season showed better fitness. More validation studies are needed in places with different ecological and climate characteristics to promote the volumetric real-time monitoring of pollens in China.
