Assessing the relationship between malaria incidence levels and meteorological factors using cluster-integrated regression.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

Characteristics of surface ozone pollution and its correlations with meteorological factors: the case of Xiangtan.

Based on ozone (O3) monitoring data for Xiangtan and meteorological observation data for 2020-2022, we examined ozone pollution characteristics and the effects of meteorological factors on daily maximum 8-h average ozone (O3-8h) concentrations in Xiangtan. Thus, we observed significant increases as well as notable seasonal variations in O3-8h concentrations in Xiangtan during the period considered. The ozone and temperature change response slope (KO3-T) indicated that local emissions had no significant effect on O3-8h generation. Further, average O3-8h concentration and maximum temperature (Tmax) values showed a polynomial distribution. Specifically, at Tmax < 27 °C, it increased almost linearly with increasing temperature, and at Tmax between 27 and 37 °C, it showed an upward curvilinear trend as temperature increased, but at a much lower rate. Then, at Tmax > 37 °C, it decreased with increasing temperature. With respect to relative humidity (RH), the average O3-8h concentration primarily exceeded the standard value when RH varied in the range of 45-65%, which is the key humidity range for O3 pollution, and the inflection point for the correlation curve between O3-8h concentration and RH appeared at ~55%. Furthermore, at wind speeds (WSs) below 1.5 m∙s-1, O3-8h concentration increased rapidly, and at WSs in the 1.5-2 m∙s-1 range, it increased at a much faster rate. However, at WSs > 2 m∙s-1, it decreased slowly with increasing WS. O3-8h concentration also showed the tendency to exceed the standard value when the dominant wind directions in Xiangtan were easterly or southeasterly.

Association of sudden sensorineural hearing loss with meteorological factors: a time series study in Hefei, China, and a literature review.

Air pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise.

An exploration of meteorological effects on PM2.5 air quality in several provinces and cities in Vietnam.

Linking meteorology and air pollutants is a key challenge. The study investigated meteorological effects on PM2.5 concentration using the advanced convergent cross mapping method, utilizing hourly PM2.5 concentration and six meteorological factors across eight provinces and cities in Vietnam. Results demonstrated that temperature (ρ = 0.30) and radiation (ρ = 0.30) produced the highest effects, followed by humidity (ρ = 0.28) and wind speed (ρ = 0.24), while pressure (ρ = 0.22) and wind direction (ρ = 0.17) produced the weakest effects on PM2.5 concentration. Comparing the ρ values showed that temperature, wind speed, and wind direction had greater impacts on PM2.5 concentration during the dry season whereas radiation had a more influence during the wet season; Southern stations experienced larger meteorological effects. Temperature, humidity, pressure, and wind direction had both positive and negative influences on PM2.5 concentration, while radiation and wind speed mostly had negative influences. During PM2.5 pollution episodes, there was more contribution of meteorological effects on PM2.5 concentration indicated by ρ values. At contaminated levels, humidity (ρ = 0.45) was the most dominant factor affecting PM2.5 concentration, followed by temperature (ρ = 0.41) and radiation (ρ = 0.40). Pollution episodes were pointed out to be more prevalent under higher humidity, higher pressure, lower temperature, lower radiation, and lower wind speed. The ρ calculation also revealed that lower temperature, lower radiation, and higher humidity greatly accelerated each other under pollution episodes, further enhancing PM2.5 concentration. The findings contributed to the literature on meteorology and air pollution interaction.

Using empirical dynamic modeling to identify the impact of meteorological factors on hemorrhagic fever with renal syndrome in Weifang, Northeastern China, from 2011 to 2020.

BACKGROUND: Hemorrhagic Fever with Renal Syndrome (HFRS) continues to pose a significant public health threat to the well-being of the population. Given that the spread of HFRS is susceptible to meteorological factors, we aim to probe into the meteorological drivers of HFRS. Thus, novel techniques that can discern time-delayed non-linear relationships from nonlinear dynamical systems are compulsory. METHODS: We analyze the epidemiological features of HFRS in Weifang City, 2011-2020, via the employment of the Empirical Dynamic Modeling (EDM) method. Our analysis delves into the intricate web of time-delayed non-linear associations between meteorological factors and HFRS. Additionally, we investigate the repercussions of minor perturbations in meteorological variables on future HFRS incidence. RESULTS: A total of 2515 HFRS cases were reported in Weifang from 2011 to 2020. The number of cases per week was 4.81, and the average weekly incidence was 0.52 per 1,000,000. The propagation of HFRS is significantly impacted by the mean weekly temperature, relative humidity, cumulative rainfall, and wind speed, and the ρCCM converges to 0.55,0.48,0.38 and 0.39, respectively. The graphical representation of the relationship between temperature (lagged by 2 weeks) and the incidence of HFRS exhibits an inverted U-shaped curve, whereby the incidence of HFRS culminates as the temperature reaches 10 °C. Moreover, temperature, relative humidity, cumulative rainfall, and wind speed exhibit a positive correlation with HFRS incidence, with a time lag of 4-6 months. CONCLUSIONS: Our discoveries suggest that meteorological factors can drive the transmission of HFRS both at a macroscopic and microscopic scale. Prospective alterations in meteorological conditions, for instance, elevations in temperature, relative humidity, and precipitation will instigate an upsurge in the incidence of HFRS after 4-6 months, and thus, timely public health measures should be taken to mitigate these changes.

Epidemic Characteristics and Meteorological Risk Factors of Hemorrhagic Fever With Renal Syndrome in 151 Cities in China From 2015 to 2021: Retrospective Analysis.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) continues to pose a significant public health threat to the population in China. Previous epidemiological evidence indicates that HFRS is climate sensitive and influenced by meteorological factors. However, past studies either focused on too-narrow geographical regions or investigated time periods that were too early. There is an urgent need for a comprehensive analysis to interpret the epidemiological patterns of meteorological factors affecting the incidence of HFRS across diverse climate zones. OBJECTIVE: In this study, we aimed to describe the overall epidemic characteristics of HFRS and explore the linkage between monthly HFRS cases and meteorological factors at different climate levels in China. METHODS: The reported HFRS cases and meteorological data were collected from 151 cities in China during the period from 2015 to 2021. We conducted a 3-stage analysis, adopting a distributed lag nonlinear model and a generalized additive model to estimate the interactions and marginal effects of meteorological factors on HFRS. RESULTS: This study included a total of 63,180 cases of HFRS; the epidemic trends showed seasonal fluctuations, with patterns varying across different climate zones. Temperature had the greatest impact on the incidence of HFRS, with the maximum hysteresis effects being at 1 month (-19 ºC; relative risk [RR] 1.64, 95% CI 1.24-2.15) in the midtemperate zone, 0 months (28 ºC; RR 3.15, 95% CI 2.13-4.65) in the warm-temperate zone, and 0 months (4 ºC; RR 1.72, 95% CI 1.31-2.25) in the subtropical zone. Interactions were discovered between the average temperature, relative humidity, and precipitation in different temperature zones. Moreover, the influence of precipitation and relative humidity on the incidence of HFRS had different characteristics under different temperature layers. The hysteresis effect of meteorological factors did not end after an epidemic season, but gradually weakened in the following 1 or 2 seasons. CONCLUSIONS: Weather variability, especially low temperature, plays an important role in epidemics of HFRS in China. A long hysteresis effect indicates the necessity of continuous intervention following an HFRS epidemic. This finding can help public health departments guide the prevention and control of HFRS and develop strategies to cope with the impacts of climate change in specific regions.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Temporal dynamics of urban air pollutants and their correlation with associated meteorological parameters: an investigation in northern Indian cities.

This study delves into the intricate dynamics of air pollution in the rapidly expanding northern regions of India, examining the intertwined influences of agricultural burning, industrialization, and meteorological conditions. Through comprehensive analysis of key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) across ten monitoring stations in Uttar Pradesh, Haryana, Delhi, and Punjab, a consistent pattern of high pollution levels emerges, particularly notable in Delhi. Varanasi leads in SO2 and O3 concentrations, while Moradabad stands out for CO levels, and Jalandhar for SO2 concentrations. The study further elucidates the regional distribution of pollutants, with Punjab receiving significant contributions from SW, SE, and NE directions, while Haryana and Delhi predominantly face air masses from SE and NE directions. Uttar Pradesh's pollution sources are primarily local, with additional inputs from various directions. Moreover, significant negative correlations (p < 0.05) between PM10, NO2, SO2, O3, and relative humidity (RH) underscore the pivotal role of meteorological factors in shaping pollutant levels. Strong positive correlations between PM2.5 and NO2 (0.71 to 0.93) suggest shared emission sources or similar atmospheric conditions in several cities. This comprehensive understanding highlights the urgent need for targeted mitigation strategies to address the multifaceted drivers of air pollution, ensuring the protection of public health and environmental sustainability across the region.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adiyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections.

BACKGROUND: While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. METHODS: Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. RESULTS: A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: -0.239 [95 % CI: -0.431 to -0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. CONCLUSIONS: This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

BACKGROUND: Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. METHOD: Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. RESULTS: A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. CONCLUSION: For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

The impact of meteorological parameters on the scrub typhus incidence in Baoshan City, western Yunnan, China.

Background: Scrub typhus has become widespread across various regions in China in recent decades, causing a considerable burden on residents. While meteorological variables significantly impact the spread of scrub typhus, there is insufficient quantitative evidence illustrating this association in known high-endemic areas. Methods: A distributed lag non-linear model was applied to explore the relationship between meteorological parameters and scrub typhus incidence from 2010 to 2019 in Baoshan City, western Yunnan Province, China. Results: High monthly mean (20°C) and maximum (30°C) temperatures were associated with a peak risk of scrub typhus in the current month. Higher minimum temperatures and higher relative humidity were followed by increasing cumulative risks over the ensuing 3 months. Higher precipitation was followed by increasing cumulative risk over the ensuing 2-month period, peaking at around 30 cm. Conclusion: The non-linear lag associations between meteorological parameters and scrub typhus incidence suggest that higher monthly minimum temperature and relative humidity could be associated with an increased risk of scrub typhus in the subsequent several months, while warm temperature is more likely to impact the occurrence of scrub typhus in the current month.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore-a stratified nationwide time-series analysis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78-0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01-1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78-0.95, RR90th percentile: 1.10, 95% CI 1.01-1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00-1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00-1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

Background: The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Methods: Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. Results: We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Conclusion: Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Trends of Mansonia (Diptera, Culicidae, Mansoniini) in Porto Velho: Seasonal patterns and meteorological influences.

Entomological research is vital for shaping strategies to control mosquito vectors. Its significance also reaches into environmental management, aiming to prevent inconveniences caused by non-vector mosquitoes like the Mansonia Blanchard, 1901 mosquito. In this study, we carried out a five-year (2019-2023) monitoring of these mosquitoes at ten sites in Porto Velho, Rondônia, using SkeeterVac SV3100 automatic traps positioned between the two hydroelectric complexes on the Madeira River. Throughout this period, we sampled 153,125 mosquitoes, of which the Mansonia genus accounted for 54% of the total, indicating its prevalence in the region. ARIMA analysis revealed seasonal patterns of Mansonia spp., highlighting periods of peak density. Notably, a significant decreasing trend in local abundance was observed from July 2021 (25th epidemiological week) until the end of the study. Wind speed was observed to be the most relevant meteorological factor influencing the abundance of Mansonia spp. especially in the Joana D'Arc settlement, although additional investigation is needed to comprehensively analyze other local events and gain a deeper understanding of the ecological patterns of this genus in the Amazon region.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5 years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2 µg/m3) and southern Lima (VMT station-126.7 µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8 µg/m3 and 34.0 µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.