Spring Seasons Research Articles

Trace Element Composition of Surface Water in Almaty City and Human Health Risk Assessment

This investigation meticulously examined the elemental composition of 64 water samples collected during the seasons of spring, summer, autumn, and winter of the year 2023. The average seasonal concentrations of arsenic (As), beryllium (Be), cobalt (Co), cadmium (Cd), copper (Cu), lithium (Li), molybdenum (Mo), nickel (Ni), lead (Pb), selenium (Se), uranium (U), mercury (Hg), aluminum (Al), barium (Ba), chromium (Cr), iron (Fe), manganese (Mn), strontium (Sr), vanadium (V), zinc (Zn), calcium (Ca), potassium (K), magnesium (Mg), sodium (Na), and chlorine (Cl) as well as SO4 and dry residue were computed at 16 strategically selected sites along the Bolshaya and Malaya Almatinka, Esentai, and Kargalinka rivers situated in Almaty. The sampling locations were categorized into three distinct sectors: upper (adjacent to mountainous regions), middle (urban zone), and lower (exceeding city limits), thereby facilitating the examination of discrepancies in water quality and elemental concentrations. The results reveal that surface water resources in Almaty, particularly concerning As, Ni, Cr, U, and Pb, may present a considerable carcinogenic risk if utilized for consumption purposes. This is especially alarming given that these rivers constitute a vital source of drinking water for the inhabitants of the city. Specifically, at two sampling locations along the Bolshaya and Malaya Almatinka rivers in proximity to significant urban thoroughfares, untreated river water displayed an elevated carcinogenic risk (CR ~ 10−2). These results highlight the urgent necessity for enhanced water treatment and ongoing monitoring to safeguard public health.

Characterization of non-Gaussianity in the snow distributions of various landscapes

Abstract. Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity.

Enhancing productivity and profitability of spring maize (Zea mays) in Uttarakhand's Tarai region through effective weed management practices and irrigation scheduling

A field experiment was conducted during the spring seasons of 2022 and 2023 at Norman E Borlaug Crop Research Centre, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand to enhance maize yield and profitability. The experiment was conducted a split-plot design (SPD) with two irrigation scheduling treatments, viz. irrigation applied at an IW/CPE ratio of 0.8 (I1); and 1.2 (I2) in main plot and 8 weed management treatments, viz. W1, atrazine 1000 g/ha; W2, tembotrione 120 g/ha; W3, topramezone 25.2 g/ha; W4, atrazine 1000 g/ ha followed by hand-weeding at 35 DAS (days after sowing); W5, atrazine 1000 g/ha followed by tembotrione 120 g/ha; W6, atrazine 1000 g/ha followed by topramezone 25.2 g/ha; W7, weed-free; and W8, weedy check in sub-plots. Irrigation applied at IW/CPE ratio of 1.2 significantly improved the maize productivity and profitability, yielding higher net returns (₹66,206.9/ha) and grain yield (6.1 t/ha). Among the herbicidal treatments, atrazine 1000 g/ha followed by topramezone 25.2 g/ha recorded the highest weed control efficiency (80%) and maize grain yield (6.3 t/ ha). The percentage net monetary return was in the following sequence: W6 (100%) > W7 (92.4%) > W5 (91.8%) > W4 (89.6%) > W3 (81.5%) > W2 (78.2%) > W1 (67.6%) > W8 (46.4%). The percentage gain in grain yield was recorded in the following sequence: W7 (100%) > W6 (91.4%) > W5 (87.4%) > W4 (86.4%) > W3 (84.1%) > W2 (81.1%) > W1 (71.5%) > W8 (52.5%). In conclusion, optimizing irrigation to IW/CPE ratio of 1.2 and employing weed management practice atrazine (1000 g/ha) followed by topramezone (25.2 g/ha) application, can significantly enhance spring maize yield, weed control efficiency and economic returns in Uttarakhand's Tarai region.

Evaluation of Chickpea Production under Different Management of Irrigation and Nitrogen Fertilizer

Background: Chickpea is a prominent leguminous crop cultivated in the West Asia and North Africa (WANA) region. Historically, it is planted during the spring season as a rainfed crop in the area, characterized by its unpredictable and frequently inadequate precipitation. Methods: To examine the impact of various levels of irrigation and nitrogen fertilizer on the yield and yield components of the Adel cultivar of chickpea, a factorial experiment was conducted based on a randomized complete block design with four replications at the research farm in Jolgeh Rokh District in Torbat-e Heydarieh (35.19 N, 59.45E) throughout the 2021 and 2022 growing seasons. The experimental treatments included three levels of nitrogen: 30, 75 and 150 kg ha-1 as urea fertilizer. The irrigation was applied at three levels: full irrigation, one supplementary irrigation at the flowering stage and two supplementary irrigations at the flowering and pod filling stages. The studied traits included grain yield, biological yield, the number of pods per plant, and the number of seeds per pod. Result: Our results showed that different levels of irrigation and nitrogen fertilizer had a significant effect on the number of seeds per pod, pods per plant and biological and grain yield. The interaction effect of irrigation and nitrogen fertilizer was significant for all studied traits. In this study, the interaction between the full irrigation treatment and 75 kg N ha-1 resulted in a significant increase in all traits (except biological yield) compared to other treatments. Furthermore, one irrigation at the flowering stage with 150 kg N ha-1 caused a significant decrease in all of the studied traits. The combination of full irrigation and 150 kg N ha-1 treatment had the highest biological yield (8.9 t ha-1), while its grain yield was 1854 kg ha-1. Therefore, by maintaining moisture levels during the critical period of chickpea growth and utilizing balanced nutritional levels, particularly nitrogen at rates of 30 and 75 kg ha-1, the yield of chickpea can be increased.

Ocular involvement of oak processionary caterpillar hairs: Clinical outcome up to one year.

To evaluate a consecutive series of patients that presented with ocular findings after contact with the oak processionary caterpillar (OPC) during an epidemic reproduction of the OPC in Germany in 2019 and to assess the 1-year outcome of those eyes with persisting OPC hairs in the cornea. Retrospective analysis of 11 eyes (11 patients) that presented in June/July 2019 with acute ocular symptoms after outdoor activity or caterpillar nest removal. Evaluation of patients charts and slit-lamp images up to one year. Assessment of the incidence of patients with OPC-associated ocular complaints in the subsequent years (2020-2023). All patients had conjunctival injection, foreign body sensation, pain, itching and/or burning sensation. In 9/11 eyes, multiple caterpillar hairs could be detected in the superficial and deeper cornea. Hair removal was attempted in 8/9 eyes; in one eye hair removal was impossible due to its deep stromal location (lost-to-follow-up). Entire hair removal was successful in 2/9 eyes, hence, six eyes with persisting hairs within the cornea were followed. Stromal haze developed adjacent to the hairs in all eyes and individual hairs disappeared (n = 4) and/or showed migration within the cornea (n = 3). Until 2023, the number of patients with OPC-associated ocular complaints decreased continuously. Ophthalmologists should be aware of the possibility of caterpillar hairs in patients with acute eye symptoms after outdoor acitivities, especially in early summer; the incidence may fluctuate, though. Hair removal is recommended to avoid possible intraocular migration, still residing hairs did not cause any serious long-term complications in our cohort. What is known Within the late spring and early summer season, mass reproduction of the oak processionary carterpillar (OPC) can lead to an increased number of patients with OPC-related eye complaints (pain, itching and burning sensation) caused by OPC hairs within a very short period of time. Within that season, people should be (more) actively sensitized to avoid trees with OPC hairs, take precautions and avoid eye rubing when having complaints What is new Complete surgical removal of the hairs can be difficult and is often unsuccessful due to their tiny size and often stromal location most possibly resulting from eye rubbing In the longer-term, residing hairs have shown to develop haze adjacent to the hair, migrate within the cornea or disappear/resolve. Since this may go along with an inflammatory reaction, long-term follow-up and topical steroids may be useful.

Seasonal anestrus of sheep flocks

Objective: To determine seasonal anestrus in relation to the season of the year, feeding type, breed, and social environment variables of sheep production systems in Singuilucan, Hidalgo, Mexico. Design/Methodology/Approach: Based on n=41 semi-structured surveys applied to the producers of the study area, a logistic regression analysis was carried out to determine the influence of the study variables on the seasonal anestrus of sheep production systems. Results: Thirty-nine production units with seasonal anestrus periods were identified. Seasonal anestrus was influenced by the season of the year, feeding type, breed, and social environment variables (P<0.05). The anestrus periods were influenced (P<0.05) by spring (season of the year), range and shed system with concentrate feeding (feeding type), wool sheep (breeds), and the presence or absence of rams in the flock (social environment). Study Limitations/Implications: Determining which variables influence seasonal anestrus will help to develop sheep reproductive programs. Findings/Conclusions: Wool sheep have a seasonal anestrus in spring. The season of the year, feeding type, breed, and social environment variables influenced the seasonal anestrus of sheep production systems in Singuilucan, Hidalgo, Mexico.

Effect of Total Chlorophyll Content and Relative Water Content of Mini Tuber Potato under Aeroponic Condition in Jammu Region, India

The present study determine the effect of Total Chlorophyll Content and Relative Water Content of Mini Tuber Potato under Aeroponic Condition in Jammu Region, India. The adoption of new production technologies, usage of better varieties, quality seed material, good storage facilities, and transportation in recent years have helped to expand area, production, and productivity in India. The present study was conducted in the Division of Plant Physiology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu. The experiment was conducted during winter and spring seasons in 2022-23. Two varieties Kufri Badshah and Kufri Jyoti were taken as experimental material. Potato tubers were sown in net pots containing growing media and seedlings were transplanted at five leaves stage in the aeroponics system. The experiment was laid out in Completely Randomized Design with two treatments viz, T1 (cocopeat 100%) and T2 (cocopeat +vermiculite+ perlite+ vermicompost) in ratio (2:1:1:2) and after two weeks of transplanting data was recorded at three different stages of crop growth (20 DAT, 45 DAT and 80 DAT). The results clearly indicated that treatment T2 coco peat + perlite + vermiculite + vermicompost in the ratio of 2:1:1:2 was the most promising treatment for improving the, biochemical parameters, physical parameters and quality of both potato varieties when compared to other treatments.

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.

Limiting Access to Resistance Training Equipment During the Off-Season: The Impact on Collegiate Pitching Metrics.

Gdovin, JR, Hogan, B, and Williams, CC. Limiting access to resistance training equipment during the off-season: the impact on collegiate pitching metrics. J Strength Cond Res XX(X): 000-000, 2024-Resistance training has been shown to improve overhead throwing velocity, but it is unknown as to how limiting access to a strength and conditioning facility affects a baseball pitcher's readiness for the spring season. Therefore, the purpose of this study was to examine the influence of an individualized pitching program on pitching metrics during an 8-week fall season in collegiate baseball pitchers without a simultaneous structured strength training program. Twelve National Collegiate Athletic Association Division-I baseball pitchers completed the study and wore a PULSE Throw Workload Monitor to track peak arm slot angle, peak arm velocity, and peak elbow varus torque, while a radar gun measured ball velocity. An 8-week pitching program had subjects throw 30 and 50 maximum effort pitches in the first and eighth week, respectively, while workload increased by 5 maximum effort pitches every 2 weeks. Paired samples t-tests were conducted to compare variables of interest before and after an 8-week fall season with an alpha level set at 0.05. There was no significant difference in peak arm slot angle, peak arm velocity, and peak elbow varus torque (p > 0.05); however, there was a significant decrease in ball velocity (p < 0.001). These data indicate that 8-week pitching programs, not coupled with a resistance training regimen, negatively affect a pitcher's ball velocity. Sport coaches and practitioners should thus make accommodations to access the necessary equipment for a pitching and strength training program to be implemented simultaneously to ensure that pitchers are prepared to perform at an optimal level during the spring season.

Drip irrigation and mulching reduce weed interference and improve water productivity of spring maize

Abstract Drip irrigation and mulching were tested to minimize unproductive water loss through evaporation and weed interference. A field experiment was conducted during spring season of 2020 and 2021 in split plot design with three replications. The study includes six treatment combinations of drip irrigation methods (surface drip and subsurface drip irrigation) and mulching (black plastic, paddy straw and no mulch) along with one conventional furrow irrigation without mulching (as control) in main plots. Four weed control treatments (atrazine 1000 g a.i./ha as pre-emergence, two hand weedings at 30 and 60 days after sowing [DAS], weed free and weedy for whole crop growth period) were kept in the subplots. The combination of drip irrigation and mulches significantly enhanced leaf area index and crop biomass at 60 DAS than furrow irrigation. Integration of subsurface drip irrigation with plastic mulching resulted in the lowest weed density and biomass among main plots. Drip irrigation coupled with plastic and straw mulching resulted in 86 and 50% reduction in weed density and biomass, respectively, as compared to no mulching. Integration of subsurface drip with paddy straw mulch and black plastic mulch resulted in 17.1 and 15.5% higher maize grain yield, respectively, as compared to furrow irrigation. The highest irrigation water productivity (3.58 kg/m3) was observed in combination of subsurface drip and paddy straw mulch followed by combination of subsurface drip and black plastic mulch (3.51 kg/m3). Overall, straw mulching in drip irrigation system proved economical in terms of maize productivity.

Assessment of the elevation-dependent warming in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021

In this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of Land surface temperature (LST) and land surface albedo (ALB) as well as aerosol optical depth (AOD). The key findings include: (1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends (2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011–2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. (3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0–2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region.

Burn Period: A use-inspired metric to track wildfire risk across Arizona and New Mexico in the southwest U.S.

Abstract Numerous tools and indices exist for wildland fire managers to anticipate and track changes in wildfire risk driven by variability in weather and climate conditions at hourly to seasonal scales. However, in working closely with southwest U.S. managers, we learned of a simple meteorological metric being informally used, but not widely accessible in existing tools or information products, to gauge short-term changes in wildfire risk. This metric, termed ‘Burn Period’ (BP), is the local count of hours per day with relative humidity values equal to or less than 20%. Our collaboration led to the development of an experimental tool called the ‘Burn Period Tracker’ to ease access and promote use of BP values for planning and response. This study is a climatological analysis of BP values at 124 fire weather stations across Arizona and New Mexico for the period 2000-2022 to aid in interpretation and understanding of this use-inspired metric. BP values reflect the strong seasonality in temperature and moisture deficit-driven wildfire risk across the southwest U.S., with risk climbing through the arid spring season, peaking in June, and then falling rapidly with the onset of the summer monsoon in July. Regression analyses show that short-term variability in BP values are driven by variability in low level atmospheric moisture in all months with strongest relationships during the summer after the onset of the monsoon. This study highlights the utility of BP as a short-term wildfire planning tool as well as an example of collaborative weather and climate services development.

Effects of individual characteristics and seasonality and their interaction on ectoparasite load of Daurian ground squirrels in Inner Mongolia, China

Understanding the drivers of parasite distribution is vital for ecosystem health, disease management, and vector monitoring. While studies note the impact of host sex, size, behavior, and season on parasite load, concurrent assessments of these factors and their interactions are limited. During the spring, summer and autumn seasons from 2021 to 2023, we trapped Daurian ground squirrel (Spermophilus dauricus), a small rodent species that inhabits eastern Asian grasslands in Inner Mongolia and collected their ectoparasites. Using machine learning Lasso regression, we pinpointed factors affecting tick and flea abundance on S. dauricus. We then analyzed these factors and their seasonal interactions with a mixed negative binomial generalized linear model. Our study revealed significant but inconsistent seasonal effects on the load of ectoparasites. The tick load was significantly higher in spring and summer compared to autumn, while the flea load was higher in summer and autumn but lacked statistical significance. Furthermore, individual factors that influence the flea and tick load were moderated by seasonal effects, with a male bias in flea parasitism observed in spring. Significant interactions were also found among seasonality, sex, and body weight. The load of male squirrel fleas was positively correlated with body weight, with the highest increase observed in spring. On the contrary, the flea load of female squirrels showed a negative correlation with body weight, significantly decreasing in the autumn with increasing weight. Significant interactions were observed between season and survival status, with hosts exhibiting higher tick load during autumn survival. Our findings underscore the importance of considering seasonal variation in parasitism and the interactions between seasonal dynamics and host biological traits in shaping parasite distributions.

Towards Real-Time Integrated Water Vapor Estimates with Triple-Frequency Galileo Observations and CNES Products

Integrated water vapor (IWV) is a crucial parameter for tropospheric sounding and weather prediction applications. IWV is essentially calculated using observations from global navigation satellite systems (GNSS). Presently, the Galileo satellite system is further developed, including more visible satellites that transmit multi-frequency signals. This study aims to evaluate the accuracy of real-time IWV estimated from a triple-frequency Galileo-only precise point positioning (PPP) processing model utilizing E1, E5a, E5b, and E5 observations, which is not addressed by the previous studies. For this purpose, Galileo datasets from 10 global reference stations spanning various 4-week periods in the winter, spring, summer, and fall seasons are acquired. To process the acquired datasets, dual- and triple-frequency ionosphere-free PPP solutions are used, including E1E5a PPP, E1E5aE5b PPP, and E1E5E5b PPP solutions. The publicly available real-time products from the Centre National d’Etudes Spatiales (CNES) are utilized. The real-time IWV values are computed and then validated with the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis products (ERA5) counterparts. The findings demonstrate that the root mean square error (RMSE) of the estimated IWV is less than 3.15 kg/m2 with respect to the ECMWF ERA5 counterparts. Furthermore, the E1E5aE5b PPP and E1E5E5b PPP models enhance the IWV’s accuracy by about 11% and 16%, respectively, compared with the E1E5a PPP model.

Energy Availability in Female Collegiate Beach Volleyball Athletes.

Willingham, BD, Daou, M, VanArsdale, J, Thomas, M, and Saracino, PG. Energy availability in female collegiate beach volleyball athletes. J Strength Cond Res 38(11): 1941-1950, 2024-Low energy availability (LEA) is a present risk for many female athletes. Yet, the literature on LEA is often reliant on single, short-term, snapshots, which may not be reflective of the chronic energy balance surrounding female athletes in sport. Therefore, the purpose of this study was to establish the current prevalence of LEA in female collegiate beach volleyball athletes during the preseason and across the competitive season. One-way repeated measures analysis of variance was used to compare measures of EA (i.e., dietary intake, activity energy expenditure [EE], body composition) and EE (i.e., resting metabolic rate and thermic effect of feeding) at 4 timepoints-once in the preseason (i.e., Fall), and 3 times across the 10-week competitive season in Spring (i.e., week 1, week 5, and week 9). Significance was set at p ≤ 0.05. Although mean EA was in the subclinical zone (i.e., 30-45 kcal·kg FFM-1·d-1) at each timepoint, the prevalence of LEA for individuals was 30.8% in the preseason, 37.5% at week 1, 25.0% at week 5, and 62.5% at week 9. Further, carbohydrate intake was below the recommended range for power athletes at each timepoint, suggesting that female beach volleyball athletes with LEA may benefit from increasing carbohydrate intake. Despite no statistical differences in body mass across the season (p = 0.577), there was a trend for increasing fat-free mass (p = 0.062) as the season progressed. Importantly, LEA is not a team-wide condition but an individual one. Therefore, approaches to mitigate LEA should be individualized.

Bioaerosol emission and exposure risk from a wastewater treatment plant in winter and spring

The potential health risks posed by bioaerosols containing pathogens originating from wastewater treatment plants (WWTPs) have gaining intensive attention. This study designated sampling locations within a WWTP situated in Xi'an, a major city in northwest China. The airborne bacterial concentration, taxonomic composition, and the associated health risks were analyzed in the aeration tanks with bottom microporous aeration system. The Anaerobic-Anoxic-Oxic (AAO) tank emitted significantly higher culturable bacteria (1.58×104 CFU m−3 in spring, 6.69×103 CFU m−3 in winter) compared to Double-ditch (DE) oxidation ditch and aerated grit chamber (AGC) chamber, aligning with 16S rDNA quantification results. The bacterial concentrations are higher in spring than that in winter, with the AAO tank posing the highest exposure risk during the spring season. The dominant genera in the air samples include Cutibacterium, Lawsonella, Acinetobacter, Pseudomonas, and Aeromonas. Among the identified genus, 139 bacterial genera were identified as potential human pathogens like Neisseria, Moraxella, Haemophilus, Escherichia-Shigella and Streptococcus. These pathogens further elevate exposure risks from WWTP bioaerosols. This study provides relevant information on the seasonal health risk variations tied to bioaerosol emissions from diverse aeration tanks with bottom microporous aeration system in the mega city of northwest China, emphasizing the imperative to enhance the management and control measures for bioaerosols originating from the AAO tank.

Influence of local meteorology and gaseous pollutant emissions on atmospheric nanoparticle concentrations in a pedestrian way in urban region

The roadside environment is one of the major sources of nanoparticle emission in the urban regions. The complex mixture of different pollutants makes it hard to understand the dynamics and behavior of nanoparticles. The study aims to analyze the dynamics of atmospheric nanoparticles ranging from 10 to 1000 nm along with local meteorological conditions and gaseous pollutants in a pedestrian way at a busy street in Delhi, a megalopolis in India, during all five major seasons of the study area. Nucleation mode particles were higher during the spring season, whereas the contribution of Aitken mode particles dominated the total particle number concentrations in the rest of the seasons. Due to the influence of vehicular sources, rush hour concentrations were higher than during non-rush hours. Thus, the diurnal pattern in particle concentration strongly coincided with emissions associated with the vehicular flow. During the winter season, the average total particle number concentration was observed to be maximum (4.1 × 104 cm-3) with a higher surface area of particles of 3.5 × 10-3 mm2m-3. Compared to the monsoon season, the concentration of NOX was 5 times higher in winter. The boundary layer height in the study region ranged from 600 to 2400 m during different seasons, and the maximum ventilation coefficient was observed to be >3000 m2s-1 during summer. Precipitation reduced the concentration of particles by half, from 2.2 x 104 to 1.1 x 104 cm-3, due to wet scavenging. The study revealed that the concentrations of particles depend not only on primary emissions but also are influenced by local meteorology and other co-emitted pollutants. Understanding the dynamics of atmospheric nanoparticles in urban roadside environments as outlined in this study is crucial to devise necessary mitigation measures for people residing near the road in order to reduce health impact and improve air quality.

Dissolved Nitrous Oxide and Methane in the Taiwan Strait: Distribution, Seasonal Variation, and Emission

AbstractThe global ocean plays an important role in the overall budgets of nitrous oxide (N2O) and methane (CH4), especially in continental estuaries and shelf areas. Four cruises were conducted between 2021 and 2022, covering the spring, summer, and fall seasons, to study the spatial and seasonal characteristics of N2O and CH4 distributions and emissions in the Taiwan Strait (TWS). The surface N2O and CH4 concentrations gradually decreased from the coast to the open sea, with maximum values (14.3 and 15.6 nmol L−1) occurring near the Jiulong and Minjiang estuaries, respectively. The mean surface N2O concentration (8.2 nmol L−1) was highest in the spring and approximately the same in the summer as in the fall. The mean surface concentrations of CH4 (8.8 nmol L−1) were greater in summer than in spring and fall, probably because of the high freshwater input in summer. Except for several stations in fall, surface waters were oversaturated with N2O and CH4 relative to the atmosphere in other seasons, and the TWS was a net source of atmospheric N2O and CH4 in the spring, summer, and fall. In situ production is the main source of N2O and CH4 in the TWS, with nitrification being the dominant mechanism of N2O production in the TWS. In contrast, physical influences (riverine inputs and water mass mixing) reshape the distributions of N2O and CH4. The annual emissions of N2O and CH4 from the TWS were estimated to be 0.9 × 10−3 ± 2.9 × 10−3 Tg yr−1 and 1.6 × 10−3 ± 3.0 × 10−3 Tg yr−1, respectively. Taken together, the TWS accounts for 0.025% of the surface area of the world's oceans and 0.022% ± 0.07% and 0.017% ± 0.033% of global oceanic N2O and CH4 emissions, respectively.

IDENTIFICATION OF CLIMATE RESILIENT GENOTYPES ON BASE OF PHYSIO-MORPHIC AND YIELD RELATED TRAITS IN MAIZE (ZEA MAYS L.)

The impact of climate change on maize production is a growing concern. Changes in temperature, precipitation patterns, and the increased frequency of extreme weather events can significantly affect maize yields. Recognizing the importance of developing climate-resilient maize varieties, an experiment was conducted in 2022 to identify such genotypes. Ten commercial maize varieties were planted with three different sowing dates in the spring season: January 15th, January 30th, and February 15th. The results indicated that Sahiwal Gold and Malka 2016 were climate-resilient and produced high grain yields compared to other varieties across the three sowing dates. The findings concluded that the January 15th sowing date was the most effective, with genotypes producing the highest yields. Physio-morphic traits such as relative water content, chlorophyll content, days to tasseling, days to silking, plant height, and yield related traits ear length, and 100-kernel weight supported the plants in producing grain yield per hectare under changing climatic conditions. The variety Pop-1 was found to be susceptible to climate change, resulting in the lowest grain yield. The selected genotypes will be valuable in breeding programs aimed at developing climate-resilient maize varieties.

Eco-Environmental Quality Assessment of Two Seasons Based on Pressure-State-Response Framework by Using Remote Sensing and GIS Techniques

Determining the urbanization and sustainable development rate in Kirkuk City and Taza District (a town located in a suburb of Kirkuk) requires an evaluation of its ecological environment quality (EEQ). This study aims to determine the ecological index (El) and assess it using the popular pressure-state-response (PSR) method based on a set of statistical and remote sensing indicators. The indicators developed by remote sensing data were obtained using Sentinel-2 satellite images to measure the quality of the environment. As new and modern technical assistance for long-term, comprehensive mapping, monitoring through GIS techniques, and the comparison of the Ecological Index in 2023 of the spring and autumn seasons, these indicators provide a basis for decision-making related to sustainable development. It demonstrates the primary relationship between human activity and health under various weather circumstances and contrasts them. It also reveals that the ecological situation is healthier in spring than in autumn (these two seasons are considered transmission periods) due to meteorological conditions, social activities, and economic activity.