Objective: To investigate the meteorological condition for incidence and spread of 2019-nCoV infection, to predict the epidemiology of the infectious disease, and to provide a scientific basis for prevention and control measures against the new disease. Methods: The meteorological factors during the outbreak period of the novel coronavirus pneumonia in Wuhan in 2019 were collected and analyzed, and were confirmed with those of Severe Acute Respiratory Syndrome (SARS) in China in 2003. Data of patients infected with 2019-nCoV and SARS coronavirus were collected from WHO website and other public sources. Results: This study found that the suitable temperature range for 2019-nCoV survival is (13-24 °C), among which 19°C lasting about 60 days is conducive to the spread between the vector and humans; the humidity range is 50%-80%, of which about 75% humidity is conducive to the survival of the coronavirus; the suitable precipitation range is below 30 mm/month. Cold air and continuous low temperature over one week are helpful for the elimination of the virus. The prediction results show that with the approach of spring, the temperature in north China gradually rises, and the coronavirus spreads to middle and high latitudes along the temperature line of 13-19 °C. The population of new coronavirus infections is concentrated in Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai and other urban agglomerations. Starting from May 2020, the Beijing-Tianjin-Hebei urban agglomeration, the Central China Zhengzhou-Wuhan urban agglomeration, the eastern Jiangsu-Zhejiang-Shanghai urban agglomeration, and the southern Pearl River Delta urban agglomeration are all under a high temperature above 24 °C, which is not conducive to the survival and reproduction of coronaviruses, so the epidemic is expected to end. Conclusions: A wide range of continuous warm and dry weather is conducive to the survival of 2019-nCoV. The coming of spring, in addition to the original Wuhan-Zhengzhou urban agglomeration in central China, means that the prevention and control measures in big cities located in mid-latitude should be strengthened, especially the monitoring of transportation hubs. The Pearl River Delta urban agglomeration is a concentrated area of population in south China, with a faster temperature rise than those in mid-high latitudes, and thus the prevention in this area should be prioritized. From a global perspective, cities with a mean temperature below 24 °C are all high-risk cities for 2019-nCoV transmission before June.

Rapid urbanization and associated transportation play a crucial role in the distribution of heavy metals in road dust, leading to serious environmental and health concerns. This study explored the concentration and spatial variability of metals, including cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn), in road dust across six urban zones in Guangdong Province (parks, educational, residential, commercial, traffic, and industrial areas). The study also evaluated the bioavailability of these metals to determine their carcinogenic and non-carcinogenic health impacts. The findings revealed significant variations in metal levels, with commercial zones having the highest concentrations of Zn 1316.1 ± 381.6 mg/kg), Cu (426.1 ± 136.7 mg/kg), and Cd (1.29 ± 0.08 mg/kg) due to the deposition of traffic emissions. The bioaccessibility of Pb, Zn, and Cd was found to be higher in the gastric phase, suggesting increased absorption potential when ingested. Children were at a significantly higher non-carcinogenic risk compared to adults, facing more than double the exposure. While the carcinogenic risk from Pb was low but notable, Cd presented minimal risk. These results highlight the need for targeted interventions, including stricter emission regulations and public health strategies, to mitigate the risks of heavy metal exposure in urban areas.

This study was aimed to determine dengue season, and further establish a prediction model by meteorological methods. The dengue and meteorological data were collected from Guangdong Meteorological Bureau and Guangdong Provincial Center for Disease Prevention and Control, respectively. We created a sliding accumulated temperature method to accurately calculate the beginning and ending day of dengue season. Probabilistic Forecast model was derived under comprehensive consideration of various weather processes including typhoon, rainstorm, and so on. We found: 1) The dengue fever season enters when effective accumulated temperature of a continuing 45 days (T ₄₅ ) ≥0 °C, and it finishes when effective accumulated temperature of a continuing 6 days (T ₆ ) <0 °C. 2) A Probabilistic Forecast Model for dengue epidemic was established with good forecast effects, which were verified by the actual incidence of dengue in Guangzhou. The Probabilistic Forecast Model provides markedly improved forecasting techniques for dengue prediction.