Influence Of Meteorological Variables Research Articles

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency.The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm).Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables.Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

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 (33years) 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.

Enhancing Accuracy of Groundwater Level Forecasting with Minimal Computational Complexity Using Temporal Convolutional Network

Multiscale forecasting of groundwater levels (GWLs) is essential for ensuring the sustainable management of groundwater resources, particularly considering the potential impacts of climate change. Such forecasting requires a model that is not only accurate in predicting GWLs but also computationally efficient, ensuring its suitability for practical applications. In this study, a temporal convolutional network (TCN) is implemented to forecast GWLs for 17 monitoring wells possessing diverse hydrogeological characteristics, located across South Korea. Using deep learning, the influence of meteorological variables (i.e., temperature, precipitation) on the forecasted GWLs was investigated by dividing the input features into three categories. Additionally, the models were developed for three forecast intervals (at 1-, 3-, and 6-month lead times) using each category input. When compared with state-of-the-art models, that is, long short-term memory (LSTM) and artificial neural network (ANN), the TCN model showed superior performance and required much less computational complexity. On average, the TCN model outperformed the LSTM model by 24%, 21%, and 25%, and the ANN model by 24%, 37%, and 47%, respectively, for 1-, 3-, and 6-month lead times. Based on these results, the proposed TCN model can be used for real-time GWL forecasting in hydrological applications.

Influence of meteorological variables on the frequency of visits and hospital admission for epistaxis in a city with a cold semi-arid climate (Lorca, Spain).

Epistaxis is a common emergency for otorhinolaryngologists. Although the aetiological factors have been widely studied, they remain subject to debate. The role of meteorological variables has been discussed in recent years, but results have been inconsistent. Given a lack of prior data, the aim of our study was to identify the meteorological variables that influence the frequency of visits and hospital admission for epistaxis in a city with a cold semi-arid climate in Spain. Case-control study. patients who attended the accident and emergency department of a secondary level hospital for epistaxis over a 9-year period (2011-2019). Controls were established by simple random sampling among emergency rooms visits in general (patients who attended the same centre over the same period of time). Sociodemographic, clinical and meteorological variables were all taken into account. 2749 patients in the epistaxis group and 2764 in the control group. There were significant differences in the epistaxis group, with a higher proportion of male (62.85%) and older patients. Univariate and multivariate analysis revealed that the daily minimum temperature and maximum wind speed were factors significantly associated with the onset of epistaxis. In addition, logistic regression analysis showed that decreases in minimum temperature and increases in maximum wind speed were associated with an increase in epistaxis (p < 0.01). No association was found between hospital admission and the weather conditions. Our findings suggest that low minimum temperatures and high wind speeds are associated with the number of visits to hospital accident and emergency departments for epistaxis, but not with hospital admission.

Investigation of spatial and temporal variations of airborne Poaceae, Myrtaceae and Cupressaceae pollen and Alternaria spores in Sydney, Australia, 2017–2020

There is a need for information on pollen exposure to assess allergy risk. Monitoring of aeroallergens in a city is usually limited to the use of a single trap for the whole area. While a single trap provides enough information on background pollen concentration for the area, varying pollen exposure across different urban environments, however, is not considered. In this study, we analysed aerobiological data of three pollen seasons (2017–2020) recorded with a volumetric pollen trap in Sydney, Australia. In order to assess spatial differences in pollen exposure across the city, we installed ten gravimetric traps recording pollen deposition for eight weeks during the summer of 2019/2020. We considered the influence of meteorological variables, land use, urbanisation and distance to the sea. Our results showed differences in pollen season characteristics across the three analysed seasons and correlations with meteorological parameters. Considering all years, we found for Poaceae and Alternaria the strongest positive correlation with mean temperature and for Myrtaceae and Cupressaceae with maximum temperature. Likewise, there were negative correlations with humidity (Myrtaceae, Cupressaceae, Alternaria) and precipitation (Myrtaceae, Cupressaceae). Days with medically relevant pollen and spore concentrations varied between years and we recorded the highest amount in 2017/2018 for Poaceae and Alternaria and in 2019/2020 for Myrtaceae. In addition, we found spatial and temporal variations of pollen deposition. However, we did not detect significant correlations between pollen deposition and land use, which can be attributable to drought conditions prior to the sampling campaign and the temporal setting in the pollen season. This study highlights the importance of continuous volumetric aerobiological monitoring as well as the assessment of pollen exposure at several locations across a large urban area.

Thermal time and extreme weather events determine the emergence of Amaranthus palmeri

Background Amaranthus palmeri has emerged as the most widespread weed of agricultural land in large parts of North and South America. Understanding its population dynamics and the influence of meteorological variables becomes important for decision-making in an integrated management context. The hypothesis is that the emergence of A. palmeri is influenced by thermal time and extreme weather events that occurred in the previous 45, 30 or 15 days. Objective The work was aimed to detect the influence of meteorological [...]

Influence of meteorological variables on air characteristics of Onitsha, Nigeria

This study evaluated the influence of prevailing meteorological characteristics on the concentration of air parameters in Onitsha metropolis, Anambra State, Nigeria in 2019. The information is required in meteorological forecasts, wind climatology, weather characteristics for planning control measures which might improve or worsen air quality, essential to evaluating air pollution control in the study area. A total of forty sampling points and one control point were selected and used for the study across Onitsha metropolis. The sampling points were selected in accordance with WHO guidelines for sampling point selection. Air quality and meteorological data were collected in-situ from primary sources in the field via portable air quality instrument and Kestrel 4500NV weather tracker. The results showed that wind direction was predominantly North-east in the dry season and South-west in the wet season. Temperature levels in the area were found to be higher in the dry season than in the wet season. Conversely, relative humidity levels were higher in the wet season than in the dry seasons. It also showed that some parameters such as SO2, NO2, H2S, VOC2, CO, PM2.5 and PM10 had higher concentrations in the dry season than in the wet season. On the other hand, TSP, PM10 and PM7 showed higher concentrations in the wet season than during dry season. High levels of measured air quality parameters were recorded around major junctions and market places within Onitsha, which are harmful to public health. Air movements influenced the fate of air pollutants; If the air was calm and pollutants couldn’t disperse, then the concentration of these pollutants would build up. On the other hand, when strong, turbulent winds blew, pollutants dispersed quickly, resulting in lower pollutant concentrations. The study further showed that transportation activities and trading activities at the market places were the main sources of high concentration levels of air parameters in the study area. Health impact assessment should be conducted in Onitsha metropolis for residents. State government should enforce compliance laws and regulate the activities of industries in the areas. The findings showed that meteorological data could help identify the source of pollutants, predict air pollution events, simulate and predict air quality using computer models. Identifying the sources means planning to reduce the impacts on air quality by anthropogenic activities.

The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities.

Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day’s number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day’s deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.

PM2.5 in Sri Lanka: Trend Analysis, Low-cost Sensor Correlations and Spatial Distribution

The South Asian island nation of Sri Lanka did not have any permanent PM2.5 monitors sharing data publicly in near-real-time until the U.S. Embassy installed a Beta Attenuation Monitor (BAM) in September 2017. This research aims to better understand the PM2.5 distribution in Sri Lanka by analyzing data collected by that BAM, and leveraging low-cost sensors, model and remote sensing data. BAM data show PM2.5 levels were “Unhealthy for Sensitive Groups” or “Unhealthy” according to the U.S. classification system, for at least 50% of the time between each November and the following February. This coincides with the northeast monsoon when stable air masses reduce dispersion of pollutants. Back trajectory analyses suggest long range transport also contributes to elevated PM2.5 during these months. Although slightly cleaner than regional embassies, this location has exceeded the Sri Lankan 24-hr standard for PM2.5 (50 µg m–3) every year since 2018. The area has met Sri Lanka’s annual standard (25 µg m–3) since 2019. We used PurpleAir (PA) and Atmos low-cost PM2.5 sensors co-located with the Embassy BAM, to develop correction factors to transform raw sensor data to BAM-like data. The influence of meteorological variables and the performance of different statistical models were considered and the regression coefficients of the most applicable models are presented. We also compared our PA correction factor against user-selectable options on the PurpleAir.com website. The Australian “Woodsmoke” correction can be applied to quickly visualize a reasonably accurate estimate of PM2.5 concentrations. We applied our PA correction factor to six other PurpleAir sensors operated around the country, to understand Sri Lanka’s PM2.5 distribution. With these corrected data, we interpolated satellite and model-derived PM2.5 annual averages at 1 km intervals. The most populated Western Province had the highest concentrations with elevated levels extending offshore. The sparsely populated southeast had the cleanest air.

The Contribution of Meteorological Parameters and the COVID-19 Partial Lockdown on Air Quality in Rio de Janeiro, Brazil

This study evaluated the pollutant levels (NO2, SO2, CO, and O3), air quality index (AQI) and the influence of meteorological variables and coronavirus disease (COVID-19) pandemic on the air quality in Rio de Janeiro. The data set used comprises periods before (March-April, 2019) and during pandemic (March-April, 2020). According to the AQI results, on most days, the air quality was ranked as “good”. Brazilian air quality standards for SO2, O3, and NO2 were not exceeded in any of the monitoring stations during partial lockdown, while CO exceeded in all periods in one site due to industrial emission. Comparing both periods, descriptive statistics for the meteorological parameters presented no differences, which suggests similar conditions. However, when evaluated week by week in 2020, weather conditions presented some differences that probably affected pollutant concentrations. The correlations between O3 and NO2 and some meteorological parameters indicate that variations in both favored ozone formation, since it is a photochemical process favored by temperature and solar radiation and that, in Rio de Janeiro, low NO2 concentrations lead to increased O3. The improvements on air quality during the partial lockdown may be attributed mainly to a reduction on emission sources rather than weather conditions.

A better understanding of air quality resulting from the effects of the 2020 pandemic in a city in the equatorial region (Fortaleza, Brazil).

The year 2020 was atypical due to the pandemic caused by the SARS-CoV-2 virus (COVID-19), providing a unique opportunity to understand changes in air quality due to the reduction in urban activity. Therefore, the aim of the present study was to perform an integrated evaluation on the influence of the effects of the 2020 pandemic on air quality in the city of Fortaleza, investigating levels of PM2.5, PM10, NO2, NO, SO2, CO, and O3, corresponding health risks, as well as the influence of meteorological variables and urban activity. In all phases analyzed, significant reductions were found in NOx, NO, NO2, and CO. A considerable reduction in PM2.5 and PM10 was found in the early phases, with an increase in the later phases. These findings are explained by the nearly 50% reduction in vehicular traffic and the consequent reduction in fossil fuel emissions, mainly in the partial lockdown and total lockdown periods, as well as reductions in commercial (stores/shops) and industrial activities. The variation in O3 was initially non-significant, followed by a considerable increase in the last three phases analyzed; this increase was influenced by changes in temperature and the incidence of sunlight. SO2 concentrations increased in the period studied, demonstrating that the vehicular fleet, local commerce, and other activities are not the predominant sources of this compound. Estimated health risks were reduced by half during the lockdown period, especially for non-smokers, followed by a drastic increase in the last three phases. The planetary boundary layer was positively correlated with O3 and PM10 and negatively correlated with NOx, NO2, and NO, indicating its influence on the distribution of pollutants in the lower atmosphere and, consequently, air quality.

Prediction of wind speed profile using two artificial neural network models: an ab initio investigation in the Bapouh’s city, Cameroon

Purpose This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. Design/methodology/approach In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. The meteorological data were collected every 10 min, at a height of 50 m from the NASA website over a period of two months from December 1, 2016 to January 31, 2017. The performance of the model was evaluated using some well-known statistical tools, such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The input variables of the model were the mean wind speed, wind direction, maximum pressure, maximum temperature, time and relative humidity. The maximum wind speed was used as the output of the network. For optimal prediction, the influence of meteorological variables was investigated. The hyperbolic tangent sigmoid (Tansig) and linear (Purelin) were used as activation functions, and it was shown that the combination of wind direction, maximum pressure, maximum relative humidity and time as input variables is the best combination. Findings Maximum pressure, maximum relative humidity and time as input variables is the best combination. The correlation between MLP and NARX was computed. It was found that the MLP has the highest correlation when compared to NARX. Originality/value Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile.

Effects of meteorological factors on the defoliation dynamics of the larch caterpillar (Dendrolimus superans Butler) in the Great Xing\u2019an boreal forests

Larch caterpillars are widely distributed in the Great Xing’an boreal forests; however, the relationship between caterpillar defoliation dynamics and climatic factors is poorly understood. The aims of this study are to investigate the primary weather conditions that might influence forest defoliation and to identify the most important life stage of the larch caterpillar at which forest defoliation might be mitigated by incorporating more inhibitory influences from climatic factors. The life cycle of the larch caterpillar was partitioned into four stages and multiple linear regression and mixed effect models were combined with a relative weight analysis approach to evaluate the importance and influence of meteorological variables on defoliation dynamics. The results show that warmer temperatures in growing seasons and overwintering periods can increase the defoliation area, while rainy and humid growing seasons decrease the defoliation area. Total precipitation during the early instar larval period had the greatest power to explain the variance in defoliation dynamics and had a very strong inhibitory effect, followed by the accumulative temperatures of the late instar larval period which had a positive impact, and precipitation during the middle instar larval period which had a negative impact. Weather conditions during the early instar larval period had the greatest influence on the area defoliated and accounted for 40% of the explained variance. This study demonstrates that climatic warming and drying will increase the risk of larch caterpillar outbreaks in the Great Xing’an Mountains.

Spatio-temporal variation of Mocis latipes (Guenée, 1852) (Lepidoptera: Erebidae) populations in Brazil according to meteorological factors

Abstract: Mocis latipes (Guenée, 1852) (Lepidoptera: Erebidae) has been recognized as a major owlet caterpillar associated to the herbivory of gramineaceous plants across the American continent. During outbreaks, the caterpillars are capable of completely consuming preferred hosts (grasses) and, when these hosts are destroyed, they can move to adjacent non-grass plants and cause similar damage. Meteorological variable such as temperature and humidity are described as factors that affect the development and abundance of M. latipes. This paper aimed to describe and compare the spatial and temporal distribution of M. latipes in different locations in Brazil and to evaluate the influence of meteorological variables on the temporal range. A total of 12 locations were evaluated, in each collection point light traps were installed near cultivated areas. In order to understand the influence of meteorological variables on the abundance of M. latipes, the data were analyzed using a Generalized Linear Model according to Poisson regression. A linear regression was also used to verify the relation between the abundance and the latitude. A total of 1,985 moths were collected. The highest collections were in Amazon and Cerrado biomes. Results show that abundance was inversely related to increasing latitude and Poisson regression analysis indicated that the main meteorological variables were significantly related to abundance at each site. This study shows that due to the high preference for gramineas and the high temperature requirements (30°C), M. latipes is an important species in hot regions and regions with hight humidity. Furthurmore, even in higher latitudes, in subtropical areas, during summer months, populations can Rapidly growth being able to cause economic damages.

Influence of meteorological variables on reference Evapotranspiration in the State of Alagoas, Brazil, based on multivariate analysis

Reference evapotranspiration (ETo) is an important agro-meteorological parameter used in several meteorological, climatological and hydrological studies. This study aimed to diagnose the effect of the meteorological variables and large-scale variability modes in ETo, based on logistic regression models. Monthly data from five conventional meteorological stations located on the State of Alagoas, Brazil, were used: Agua Branca, Maceio, Pao de Acucar, Palmeira dos Indios and Porto de Pedras, from 1960 to 2016 period. The variables used were as follows: temperatures (air—Tair, maximum—Tmax and minimum—Tmin), relative humidity (RH), sea level pressure, sunshine, wind speed (WS), rainfall, evaporation and also two climatic proxies: Oceanic Nino Index (ONI) and Tropical South Atlantic (TSA). ETo was estimated using the Penman–Monteith FAO method. The binary logistic regression procedure evaluated ETo values below the percentile 75 (ETo75 characterized as 1). Regarding the variables used the percentile 75 (except Tair and rainfall that used the percentile 30). The temperatures were the main variable for ETo75 events, except in Agua Branca. The Tmax presented the highest odds ratio (OR) in Palmeira dos Indios with value of 21.10 times on occurrence of ETo75. The wind is the most important variable in Pao de Acucar, due the heat capacity of the Sao Francisco River, with OR value of 3.72 times. The ONI did not integrate the models. Based on AIC and ROC curve values, Palmeira dos Indios presented the best model with a value of 195.95 and 0.979, respectively.

Individual-tree diameter growth model for Korean pine plantations based on optimized interpolation of meteorological variables

To explore the influence of meteorological variables on the growth of Korean pine (Pinus koraiensis Sieb. et Zucc.) plantations and provide a scientific reference for the production and management of Korean pine, three approaches to interpolate meteorological variables during the growing season (i.e., May–September) were compared in Heilongjiang Province, China. Optimized meteorological variable interpolation results were then combined with stand and individual tree variables, based on data from 56 sample plots and 2886 sample trees from Korean pine plantations in two regions of the province to develop an individual-tree diameter growth model (Model I) and an individual-tree diameter growth model with meteorological variables (Model II) using a stepwise regression method. Moreover, an individual-tree diameter growth model with regional effects (Model III) was developed using dummy variables in the regression, and the significance of introducing these dummy variables was verified with an F-test statistical analysis. The models were validated using an independent data set, and the predictive performance of the three models was assessed via the adjusted coefficient of determination ( $${R}_{a}^{2}$$ ) and root mean square error (RMSE). The results suggest that the growth increment in tree diameter of Korean pine plantations was significantly correlated with the natural logarithm of initial diameter (ln D), stand basal area (BAS), logarithmic deformation of the stand density index (ln SDI), ratio of basal area of trees larger than the subject tree to their initial diameter at breast height (DBH) (BAL/D), and the maximum growing-season precipitation (Pgmax). The individual-tree diameter growth models of Korean pine plantations developed in this study will provide a good basis for estimating and predicting growth increments of Korean pine forests over larger areas.

La concentración de partículas en el aire: análisis estadístico de la relación espacial entre medidas de superficie y del sensor MODIS para dos tipos de tiempo en la Comunidad de Madrid

Los sensores remotos están monitoreando la contaminación de la atmósfera planetaria y generando una abundante información sobre los aerosoles / partículas, entre otros componentes, como sucede con el sensor MODIS y su indicador Aerosol Optical Depth (AOD). Su cotejo con las clásicas medidas en estaciones de superficie suscita problemas de discordancias entre ambos tipos de datos. Asumiendo que debería existir una relación positiva entre ellas, en este trabajo se acomete un análisis sistemático de datos para dos tipos de tiempo, especialmente proclives a altas concentraciones de partículas en el aire en la región de Madrid, para evaluar la intensidad de la correlación entre AOD y PM10 superficial y explorar sus posibles variaciones en función de ciertas variables atmosféricas (temperatura, viento y humedad relativa), de los tipos de tiempo y del contexto geográfico de cada estación de medida. En consonancia con los hallazgos de otros autores, los resultados de este trabajo corroboran, en general, la existencia de tal relación positiva, aunque afloran inconsistencias y frecuentes casos en los que la relación estadística se reduce y llega a desvanecerse por razones presumiblemente diversas, entre otras la notable falta de datos.

Weather generator で生成した気象値が生態系プロセスモデルによるバイオマスおよび水収支推定に与える影響

Weather generator で生成した気象値が生態系プロセスモデルによるバイオマスおよび水収支推定に与える影響

Influence of Meteorological Variables on Suburban Atmospheric PM2.5 in the Southern Region of Peninsular Malaysia

ABSTRACT Air pollution is a crucial contributor to premature mortality and health problems. The excessive inhalation of fine particulate matter (PM2.5) is strongly associated with adverse health effects due to its capability to penetrate deep into the human respiratory system. This study aimed to analyze the seasonal cycles of 24 h average PM2.5 mass concentrations in a suburban area in the southern region of Peninsular Malaysia. The meteorological variables and PM2.5 data were obtained via a Grimm Environmental Dust Monitor from August 2017 until January 2018. The maximum 24 h mass concentration was 44.6 µg m–3, with a mean value of 21.85 µg m–3, which was observed during the southwest monsoon . 43.33% and 8.33% of the daily concentrations exceeded the 24 h World Health Organization Guideline and Malaysian Ambient Air Quality Standard, respectively. The variation in the PM2.5 mass ranged between 0.53 and 0.90 times of the PM10 mass, indicating that the PM2.5 consistently contributed 52–92% of the PM10 mass concentration. During the monsoon seasons, the ambient temperature exhibited a significant positive correlation (p < 0.05) with the PM2.5 mass concentration (r = 0.425–0.541), whereas the wind speed (r = –0.23 to –0.0127) and the relative humidity (r = –0.472 to –0.271) displayed strong negative correlations with it. Additionally, the rainfall was weakly correlated with the mass concentration. The presence of northeasterly wind at the study site suggests that the PM2.5 originated from sources to the northeast, which are influenced by anthropogenic activities and high traffic.

The influence of meteorological variables on the oviposition dynamics of Aedes aegypti (Diptera: Culicidae) in four environmentally distinct areas in northeast Brazil.

BACKGROUND Fluctuations in climate have been associated with variations in mosquito abundance.OBJECTIVES To analyse the influence of precipitation, temperature, solar radiation, wind speed and humidity on the oviposition dynamics of Aedes aegypti in three distinct environmental areas (Brasília Teimosa, Morro da Conceição/Alto José do Pinho and Dois Irmãos/Pintos) of the city of Recife and the Fernando de Noronha Archipelago northeastern Brazil.METHODS Time series study using a database of studies previously carried out in the areas. The eggs were collected using spatially distributed geo-referenced sentinel ovitraps (S-OVTs). Meteorological satellite data were obtained from the IRI climate data library. The association between meteorological variables and egg abundance was analysed using autoregressive models.FINDINGS Precipitation was positively associated with egg abundance in three of the four study areas with a lag of one month. Higher humidity (β = 45.7; 95% CI: 26.3 - 65.0) and lower wind speed (β = −125.2; 95% CI: −198.8 - −51.6) were associated with the average number of eggs in the hill area.MAIN CONCLUSIONS The effect of climate variables on oviposition varied according to local environmental conditions. Precipitation was a main predictor of egg abundance in the study settings.