World Meteorological Day

This study thoroughly examined the impact of coal burning on the emission of polycyclic aromatic hydrocarbons (PAHs) and their subsequent long-lasting presence in different environmental matrices, including road dust. The aim was to identify the origin, extent, spatial arrangement, and cancer-causing potential associated with PAHs in road dust in Changchun City, Northeast China. The levels of Σ16 PAHs in the road dust samples ranged from 0.314 to 17.418 mg/kg, with an average concentration of 1.4452 mg/kg, exhibiting lower values than cities worldwide. In Changchun City, PAH levels in various regions follow the order of Chaoyang (CY) > Lvyuan (LY) > Kuancheng (KC) > Jingyue (JY) > Nanguan (NG) > Er’dao (ErD). Road dust primarily comprised PAHs containing 4–5 rings among these substances. The incremental lifetime cancer risk (ILCR) analysis indicated that adults faced potential risk (>10−6) at 97.7% of the sampling sites, while children faced potential risk (>10−6) ...

  Incorporating large-scale climate indices like the El Niño/Southern Oscillation (ENSO) is essential for understanding climate variability and change on a finer scale . Therefore, this study aimed to investigate the trends and magnitude of climate variability and change in the Kembata Tembaro zone in Southern Ethiopia. Climate data from the Kadida Gamella (KG), Kacha Birra (KB), and Hadaro Tunto (HT) stations were collected. The coefficient of variation (CV), standardized anomaly index (SAI), and standard precipitation index (SPI) were used to assess the climate variability. The Pearson product moment correlation was used to determine the association between rainfall variability and ENSO. In addition, the Mann–Kendall (MK) trend test was used to assess climate trends. The results revealed that rainfall variability was observed between seasons, with CVs ranging from 14.1% to 25.0%. Higher percentages of dry (negative) rainfall anomaly values over time were estimated during the Kire...

By using the hourly visibility, temperature, pressure, humidity, wind, and atmospheric particulate concentration data in Chongqing from 2015 to 2023, the characteristics of low visibility (visibility <1000 m) in Chongqing and the influence of various factors on low visibility in Chongqing were analyzed. The visibility prediction model was established by using the neural network method, and the effect of introducing the PM2.5 concentration factor on low visibility prediction was analyzed and compared. Findings: Low visibility in Chongqing is dominated by precipitation low visibility (PLV), followed by fog low visibility (FLV), with the least proportion of fog-haze mixed low visibility (FHLV). However, as visibility decreases further, the proportion of fog with low visibility increases significantly. The average visibility when fog occurs is lower than that when precipitation occurs and also much lower than that of fog-haze mixed, indicating that low visibility is more affected by at...

Machine learning has achieved significant success in many statistical application scenarios, but has yet to be fully successful in monthly and seasonal predictions. We identified three statistical challenges in climate prediction: instability of statistical models, complexity of feature factors, and the nonlinearity of the relationship between predictors and predictands. These characteristics limit both traditional empirical forecasting and machine learning methods. This paper proposes a novel method called dynamically modeled machine learning to predict monthly temperature anomalies over China. The core idea of dynamic modeling is that the machine learning model is trained using a sliding time window, so that the relationship between predictors and predictands is optimized for a specific and recent period rather than for the entire time span. One hundred thirty indices related to atmospheric and oceanic circulation and other climatic events from the Beijing Climate Center are used as...