International Journal of Biometeorology最新文献

Global warming has intensified the frequency and severity of drought events worldwide, posing a severe threat to agricultural productivity. Northeast China (NEC), a paramount soybean base, is particularly vulnerable to this agrometeorological disaster. The development of soybean drought indicators in NEC, based on comprehensive analysis of disaster processes, would greatly enhance dynamic monitoring and early warning systems for soybean drought. This research has significant implications for regional drought prevention and effective disaster mitigation strategies. In this study, we eliminated the spatial variability of the water surplus and deficit index ([Formula: see text]) and constructed a new soybean water surplus and deficit index ([Formula: see text]). By inverting the historical drought disaster process of soybean drought, we determined the initial discriminant value ([Formula: see text]) of drought. The Kolmogorov‒Smirnov (K-S) test was conducted to determine the optimal distribution model of the sample sequence, and the t-distribution interval estimation method was used to obtain the indicator level threshold. Based on the newly constructed soybean drought indicators, soybean drought risk assessments were carried out. The findings indicated that the drought duration days ([Formula: see text]) estimated based on [Formula: see text], as the dominant factor, and the daily cumulative value ([Formula: see text]) with [Formula: see text], as the auxiliary factor, could be used to more accurately monitor soybean drought in NEC. The accuracy rate of the indicators was 82.4%. There were spatial differences in the probability of each drought level. In terms of the drought risk level, the high-risk area was distributed mainly in the eastern part of Heilongjiang Province, and the low-risk area was distributed mainly in the central and western parts of the East Four Leagues, the western part of Liaoning Province, and a small part of Heilongjiang and Jilin Provinces.

Influenza remains a major global public health concern, and growing evidence suggests that air pollution may influence its incidence. However, most existing studies have relied on syndromic surveillance data, limiting the validity of their findings. This study investigates the association between short-term exposure to six ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) and laboratory-confirmed influenza cases in Shanghai from 2013 to 2017. Using a time-stratified case-crossover design and individual-level exposure estimates derived via inverse distance weighting, we evaluated multiple lag structures to characterize exposure-response relationships. Results indicated that elevated concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO were significantly associated with increased influenza risk, while higher O₃ levels were linked to a reduced risk. Specifically, a 10 µg/m³ increase in PM₁₀ concentration was associated with an elevated influenza risk (OR = 1.019, 95% CI: 1.004-1.034), whereas a corresponding increase in O₃ concentration was associated with a 4.1% reduction in risk (OR = 0.959, 95% CI: 0.941-0.977). Positive associations for the other pollutants were consistently observed across multiple single-day and moving average lag periods. Effect estimates exhibited marked heterogeneity by demographic characteristics, season, and city, with notable differences observed between Shanghai, Beijing, and Shenzhen. Our findings highlight the importance of considering lag effects, nonlinear exposure patterns, and regional variability in assessing the health impacts of air pollution. This study offers robust evidence to support targeted air quality management and influenza prevention strategies.