Observation Sites Research Articles

Enhanced VOC emission with increased temperature contributes to the shift of O3-precursors relationship and optimal control strategy

Assessing the impact of anthropogenic volatile organic compounds (VOCs) on ozone (O3) formation is vital for the management of emission reduction and pollution control. Continuous measurement of O3 and the major precursors was conducted in a typical light industrial city in the YRD region from 1 May to 25 July in 2021. Alkanes were the most abundant VOC group, contributing to 55.0% of TVOCs concentration (56.43 ± 21.10 ppb). OVOCs, aromatics, halides, alkenes, and alkynes contributed 18.7%, 9.6%, 9.3%, 5.2% and 1.9%, respectively. The observational site shifted from a typical VOC control regime to a mixed regime from May to July, which can be explained by the significant increase of ROx production, resulting in the transition of environment from NOx saturation to radical saturation with respect to O3 production. The optimal O3 control strategy should be dynamically changed depending on the transition of control regime. Under NOx saturation condition, minimizing the proportion of NOx in reduction could lead to better achievement of O3 alleviation. Under mixed control regime, the cut percentage gets the top priority for the effectiveness of O3 control. Five VOCs sources were identified: temperature dependent source (28.1%), vehicular exhausts (19.9%), petrochemical industries (7.2%), solvent & gasoline usage (32.3%) and manufacturing industries (12.6%). The increase of temperature and radiation would enhance the evaporation related VOC emissions, resulting in the increase of VOC concentration and the change of ROx circulation. Our results highlight determination of the optimal control strategies for O3 pollution in a typical YRD industrial city.

The impact of aerosols and model grid spacing on a supercell storm from Swabian MOSES 2021

AbstractThe supercell storm that occurred in southwestern Germany on June 23, 2021, had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. During that summer, the Swabian MOSES field campaign was held in that area, and several hydro‐meteorological measurements are available, as the storm passed directly over the main observation site. We present hindcasts of this event with the Icosahedral Non‐hydrostatic model using two horizontal grid spacings (i.e., 2 km, 1 km) with a single‐moment and an advanced double‐moment microphysics scheme. Numerical results show that all 2 km model realizations do not simulate convective precipitation at the correct location and time. For the 1 km grid spacing, changes in aerosol concentration resulted in large changes in convective precipitation. Only the 1 km run assuming a low cloud condensation nuclei (CCNs) concentration is able to realistically capture the storm, whereas no supercell is simulated in the more polluted scenarios. Observed aerosol particle concentrations indicate that CCNs values were the lowest of the month, which suggests that the low aerosol concentration is a reasonable assumption. The thermodynamic structure of the pre‐convective environment, as well as other observations, showed the best agreement to this model run as well, indicating that the good representation of the supercell was obtained for the right reason. The automatic tracking of individual clouds revealed that more convective cells with longer lifetimes are simulated at finer resolution. We also find a negative aerosol–precipitation effect that is not only due to a reduced collision–coalescence process, but also to weaker cold‐rain processes. These findings demonstrate the benefits of using an aerosol‐aware double‐moment microphysics scheme for convective‐scale predictability and that the use of different CCNs concentrations can determine whether a supercell is successfully simulated or not.

Future flood envelope curves for the estimation of design flood magnitudes for highway bridges at river crossings

Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows, are commonly used by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is 75-year flood magnitude in Canada. This study proposes a novel framework for climate change adaption of Creager curves for estimating future design floods. These curves, for the current period, are assessed considering 417 observation stations, located in seven major Canadian river basins (i.e., Fraser, Nelson, Mackenzie, Yukon, Churchill, St Lawrence and St John). The Creager coefficient C, which defines flood envelope curves, varies between 1 and 45 across the studied river basins. To adapt Creager curves for future changes in streamflow, a correction factor, RC, which is the ratio of future to current period C values, is proposed. These factors are obtained for observation sites, using streamflow data from an ensemble of Regional Climate Model (RCM) simulations for current and future periods, through two Regional Frequency Analysis approaches. The first approach, considering only the RCM cells where the stations are located, suggests RC in the 0.3–1.6 range, with southeasterly basins showing values < 1. The second approach, considering all RCM cells for a given region, yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations. From a practical viewpoint, the proposed framework for estimating future design floods is robust and transferrable to other basins, but can benefit using streamflow projections from other models for better uncertainty quantification.

The thermal effect of snow cover on ground surface temperature in the Northern Hemisphere

Snow cover is critical to the ground thermal regime because it affects surface conditions and the energy balance. Prior work inherently included confounding effects from vegetation and the soil when estimating the snow’s effect by using the differences between air and ground surface or soil temperature. Here we use the Kudryavtsev model including a snow module to isolate the effect of only the snow cover during winter, based on observational sites across the Northern Hemisphere. We find that snow’s damping of annual mean ground surface temperature averages 5.06 ± 3.15 °C and ranges 0 °C–10 °C, while damping of the annual ground surface temperature amplitude is 7.95 ± 4.95 °C, ranging 0 °C–20 °C. Greater insulation occurs in the high-latitudes. The insulating effect is primarily driven by snow depth, but also the combination of air temperature and its amplitude, snow depth, and duration. Snow cover duration and low air temperature enhance snow’s insulating ability. These observational results that isolate the damping effect of only the snow could play a significant role in better understanding the energy change between air and the ground.

Net Isotopic Signature of Atmospheric CO2 Sources and Sinks: No Change since the Little Ice Age

Recent studies have provided evidence, based on analyses of instrumental measurements of the last seven decades, for a unidirectional, potentially causal link between temperature as the cause and carbon dioxide concentration ([CO2]) as the effect. In the most recent study, this finding was supported by analysing the carbon cycle and showing that the natural [CO2] changes due to temperature rise are far larger (by a factor &gt; 3) than human emissions, while the latter are no larger than 4% of the total. Here, we provide additional support for these findings by examining the signatures of the stable carbon isotopes, 12 and 13. Examining isotopic data in four important observation sites, we show that the standard metric δ13C is consistent with an input isotopic signature that is stable over the entire period of observations (&gt;40 years), i.e., not affected by increases in human CO2 emissions. In addition, proxy data covering the period after 1500 AD also show stable behaviour. These findings confirm the major role of the biosphere in the carbon cycle and a non-discernible signature of humans.

Atmospheric CO2 in the megacity Hangzhou, China: Urban-suburban differences, sources and impact factors

Limited observation sites and insufficient monitoring of atmospheric CO2 in urban areas restrict our comprehension of urban-suburban disparities. This research endeavored to shed light on the urban-suburban differences of atmospheric CO2 in levels, diurnal and seasonal variations as well as the potential sources and impact factors in the megacity of Hangzhou, China, where the economically most developed region in China is. The observations derived from the existing Hangzhou Atmospheric Composition Monitoring Center Station (HZ) and Lin'an Regional Atmospheric Background Station (LAN) and the newly established high-altitude Daming Mountain Atmospheric Observation Station (DMS), were utilized. From November 2020 to October 2021, the annual averages of HZ, LAN and DMS were 446.52 ± 17.01 ppm, 441.56 ± 15.42 ppm, and 422.02 ± 10.67 ppm. The difference in atmospheric CO2 mole fraction between HZ and LAN was lower compared to the urban-suburban differences observed in other major cities in China, such as Shanghai, Nanjing, and Beijing. Simultaneous CO2 enhancements were observed at HZ and LAN, when using DMS observations as background references. The seasonal variations of CO2 at LAN and DMS exhibited a high negative correlation with the normalized difference vegetation index (NDVI) values, indicating the strong regulatory of vegetation canopy. The variations in boundary layer height had a larger influence on the low-altitude HZ and LAN stations than DMS. Compared to HZ and LAN, the atmospheric CO2 at DMS was influenced by emissions and transmissions over a wider range. The potential source area of DMS in autumn covered most areas of the urban agglomeration in eastern China. DMS measurements could provide a reliable representation of the background level of CO2 emissions in the Yangtze River Delta and a broader region. Conventional understanding of regional CO2 level in the Yangtze River Delta through LAN measurements may overestimate background concentration by approximately 10.92 ppm.

Correlation between Pre-Operative Myocardial Fibrosis and Early Post CABG Dysrhythmia

Background: Cardiac magnetic resonance (CMR) imaging combines the assessment of both the functional and structural aspects of the heart in order to identify the existence, timing, and intensity of ischemic heart disease by analyzing the function of the myocardium and the movement of the heart wall. This study aimed to investigate whether preoperative myocardial fibrosis, measured by CMR imaging, may be used to predict the incidence of rhythm disturbances in the early postoperative phase after coronary artery bypass grafting (CABG) surgery.&#x0D; Methods: Two groups of 92 patients who had CABG procedures performed were studied in this retrospective observational single site cohort study: There were 43 patients in Group A who had atrial or ventricular arrhythmia, and 49 patients in Group B who did not.&#x0D; Results: There was no correlation between arrhythmia and non-arrhythmia group and age, sex, body mass index, risk factor, CMR timing before surgery, cross clamp time, bypass time, left ventricular end-diastolic volume index (LV EDVI), LV end-systolic volume index (ESVI), stroke volume index (SVI), LV ejection fraction (EF) and territory of scar and were positive correlation between both groups and scar (P &lt;0.001). Scar% was an independent predictor of occurrence of rhythm disturbance (P=0.002) while LV EDVI, LV EF, LV ESVI, SVI, and presence of scar were not. Group A had a statistically significantly lower LV-EF% and lower LV-SVI compared to Group B. Group A had a higher scar percentage compared with group B and this was statistically significant (P &lt;0.001). Rhythm disturbance occurred more often in patients with a scar percentage &gt;14.8% {P=0.0002 and area under ROC curve (AUC)=0.708}.&#x0D; Conclusions: CMR has evolved as a gold standard non-invasive imaging tool in cardiovascular medicine. Preoperative CMR imaging may be a promising tool for predicting postoperative cardiac arrhythmia after CABG. Our study showed that preoperative myocardial scarring &gt;14.8%, as determined by CMR imaging, was predictive of early postoperative arrhythmia in patients undergoing CABG.

Pollution Characteristics, Source Apportionment, and Meteorological Response of Water-soluble Ions in PM2.5 in Xinxiang, North China

Pollution variation, source characteristics, and meteorological effects of water-soluble inorganic ions (WSIIs) in PM2.5 were analyzed in Xinxiang city, Henan Province. PM2.5 samples and their chemical components were monitored online by using URG-9000 in four seasons:winter (January, 2022), spring (April, 2022), summer (July, 2022), and fall (October, 2022). The results showed that the TWSIIs had the same seasonal fluctuations as PM2.5. The average seasonal concentrations of WSIIs ranged from 19.62-72.15 μg·m-3, accounting for more than 60% of PM2.5, demonstrating that WSIIs were the major components of PM2.5. The annual concentration value of NO3-/SO42- was 2.11, which showed an increasing trend, suggesting predominantly mobile sources for secondary inorganic aerosols (SNA). Further, the molar concentration value [NH4+]/[NO3-] was 1.95, demonstrating that agriculture emissions were the dominant contributors to atmospheric nitrogen. Furthermore, the backward trajectory analysis showed that the concentrations of Ca2+ and Mg2+ were higher when the northeasterly wind prevailed and the wind speed was high. High values of SOR and NOR were correlated with low temperatures and high relative humidity (T < 8℃, RH > 60%), demonstrating that more gaseous precursors were converted into sulfate and nitrate. At high temperatures (T > 24℃), there was no apparent high NOR value like that for SOR, mainly due to the decomposition of NH4NO3 at high temperatures. Finally, backward trajectories associated with the PMF-resolved results were used to explore the regional transport characteristics. The results illustrated that dust sources in the study areas were mainly influenced by air trajectories originating from the northwest regions, whereas secondary sulfate, secondary nitrate, and biomass sources contributed more to WSIIs when wind speed and altitude air masses were low in the area surrounding the observation site.

Spatiotemporal characteristics and variability in the thermal state of permafrost on the Qinghai–Tibet Plateau

AbstractPermafrost degradation on the Qinghai–Tibet Plateau (QTP) has significant impacts on climate, hydrology, and engineering and environmental systems. To understand the temporal and spatial characteristics of permafrost on the QTP, we quantified the variation in active layer thickness (ALT), permafrost thermal state, and future permafrost change under different scenarios using observational data, reanalysis data, and the numerical permafrost model. Generally, ALT ranged from 0.5 to 6.0 m with an average of 2.39 m, and mean annual ground temperature (at a depth of zero annual amplitude for ground temperature) mainly ranged between 0 and −3°C with an average of −0.85°C. The soil temperatures in different layers based on the ERA5‐Land data revealed even stronger increasing trends, for example, 0.245, 0.245, 0.244, and 0.238°C/decade at depths of 0–7, 7–28, 28–100, and 100–289 cm from 1980 to 2021, compared to those during the period from 1960 to 2021, which were 0.153, 0.156, 0.155, and 0.149°C/decade, respectively. The average warming trends in annual mean soil temperature were 0.153 and 0.243°C/decade from 1960 to 2021 and 1980 to 2021, respectively. The average rate of thickening of the ALT among the 10 active layer observation sites was 2.84 cm/year. There was a significant warming trend in ground temperature above ~15 m with warming of 0.063 to 0.120, 0.026 to 0.182, 0.101 to 0.314, and 0.189 to 0.303°C/decade at the QTB01, QTB06, QTB08, and XDTGT sites, respectively, and yearly minimum ground temperatures exhibited stronger warming trends than maximum ground temperatures. In addition, the simulation revealed significant increases in ground temperature at the Xidatan (XDT) and Tanggula (TGL) sites under both historical and future Representative Concentration Pathway (RCP) scenarios, but the increases in ground temperature were significantly greater at TGL than XDT. These findings provide important information for understanding the variability in permafrost degradation processes and improving simulations of permafrost change under climate change on the QTP.

Temporal Scaling Characteristics of Sub‐Daily Precipitation in Qinghai‐Tibet Plateau

AbstractThe Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions.

Abundance assessment of Sulawesi Banded Swallowtail, Papilio gigon gigon and availability of resource plants in Bantimurung Bulusaraung National Park, South Sulawesi, Indonesia

Abstract. Ismail T, Maulany RI, Ngakan PO. 2024. Abundance assessment of Sulawesi Banded Swallowtail Papilio gigon gigon and availability of resource plants in Bantimurung Bulusaraung National Park, South Sulawesi, Indonesia. Biodiversitas 25: 673-682. The Sulawesi Banded Swallowtail (Papilio gigon gigon Felder &amp; Felder, 1864) is an endemic butterfly species of Sulawesi, Indonesia currently under threat from hunting and trade activities and habitat degradation. This study aims to examine the abundance of P. gigon gigon and resource plants' availability and to analyze the relationship between resource host and nectar plant density with the abundance of the butterfly species in Bantimurung Bulusaraung National Park, South Sulawesi, Indonesia. Data were collected using the line transect method in two habitat types: settlement and forest located in three observation sites (Bantimurung, Leang-leang, and Pattunuang). The data were analyzed with Spearman's correlation test. The results showed a significant positive correlation in Bantimurung between the host plant densities (r = 0.258; p = 0.010) and nectar plants (r = 0.211; p = 0.036) with the abundance of P. gigon gigon. In Pattunuang, the host plants had indicated an insignificant correlation (r = 0.061; p = 0.548) with the abundance of P. gigon gigon, and in contrast, the nectar plants showed a significant positive correlation (r = 0.487; p = 0.000). Only in Leang-leang the correlation between the abundance of P. gigon gigon showed no significant correlation with both host (r = 0.152; p = 0.132) and nectar plants (r = 0.041; p = 0.686). Differences between the observation areas were suspected to be influenced by human activities such as tourism and hunting. Therefore, management interventions by the national park are required to support the conservation of P. gigon gigon in the area.

Estimation of actual evapotranspiration from different ecosystems on the Tibetan Plateau based on a generalized complementary evapotranspiration theory model

AbstractActual evapotranspiration constitutes a vital component of the exchange of energy and water vapour between the soil‐vegetation and atmospheric systems on terrestrial terrain. Nevertheless, the Tibetan Plateau, owing to its austere environmental conditions, harbours a scarcity of terrestrial monitoring stations. This circumstance presents a formidable challenge in attaining precise estimations of actual evapotranspiration. The complementary relationship method is a potential approach because it requires only routine meteorological data to estimate actual evapotranspiration on a regional or global scale. However, the suitability of the complementary relationship model across diverse ecosystems on the Tibetan Plateau necessitates further investigation. In this study, we scrutinized the simulation of daily and monthly actual evapotranspiration across 18 observation sites spanning eight distinct land use categories on the Tibetan Plateau. We employed the polynomial generalized complementary function introduced by Brutsaert (B2015), alongside its enhanced rendition proposed by Szilagyi (S2017) and Crago (C2018). The outcomes reveal that all three models adeptly replicate the fluctuations in actual evapotranspiration, irrespective of land use category or temporal scale—whether daily or monthly. This is true regardless of whether original or calibrated parameter values are applied. However, there exist significant variations in the performance of these models. In general, the C2018 model demonstrates superior performance across most ecosystems when original parameters are employed. Following parameter calibration, the simulation efficacy of the models experienced marked enhancement. Post parameter calibration, the B2015 model outperforms the other two models notably in desert and wetland environments. Furthermore, the simulation outputs from all three models display heightened sensitivity to parameter α, particularly in the context of the C2018 and S2017 models. These findings suggest that accurate estimation of parameter values is critical to improving the accuracy of estimating actual evapotranspiration. Calibrated parameter values, contingent on a fusion of vegetation, meteorology and surface roughness, exhibit variability across diverse ecosystems.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r&gt;0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

Smart Motorcycle Helmet: Utilizing CNN for Multi-Task Learning

Enhancing road safety through education and enforcement relies on automated motorcycle helmet detection via video surveillance. However, existing methods encounter challenges like tracking individual motorcycles and distinguishing riders from passengers. To overcome these limitations, we propose a CNN-based multi-task learning (MTL) method. Our approach focuses on identifying and tracking individual motorcycles, particularly emphasizing rider-specific helmet use. We introduce the HELMET dataset, containing 91,000 annotated frames from 10,006 motorcycles across 12 observation sites in Myanmar, serving as a benchmark for future detection techniques. Leveraging concurrent visual similarity learning and helmet use classification, our MTL approach achieves enhanced efficiency and accuracy, operating at over 8 FPS on consumer hardware. With a weighted average F-measure of 67.3% for detecting riders and helmet use, our method underscores deep learning's accuracy and resource efficiency in gathering critical road safety data. Furthermore, we present an intelligent motorcycle helmet equipped with infrared transceivers, an image sensor, an embedded computation module, a charging module, a microphone, and earphones. Designed for large vehicle approach notification, the helmet utilizes image recognition modes for both day and night conditions. Experimental results demonstrate successful vehicle registration plate recognition for large trucks/buses, achieving up to 75% accuracy during the day and 70% at night. In real-time, the proposed intelligent motorcycle helmet detects approaching large vehicles within a 5-meter distance.

A carbonate system time series in the Eastern Mediterranean Sea. Two years of high-frequency in-situ observations and remote sensing

The rate of ocean uptake of anthropogenic CO2 has declined over the past decade, so a critical question for science and policy is whether the ocean will continue to act as a sink. Large areas of the ocean remain without observations for carbonate system variables, and oceanic CO2 observations have declined since 2017. The Mediterranean Sea is one such an area, especially its eastern part, where there is a paucity of carbonate system data, with large areas not sampled or only sampled by ship-based discrete measurements as opposed to high frequency, sensor-equipped time-series fixed stations. The aim of this study was to analyze a multi-year time-series of high-frequency (hourly) partial pressure CO2 (pCO2) and pH measurements in the Eastern Mediterranean, along with low-frequency (monthly) measurements of total dissolved inorganic carbon and total alkalinity. The pCO2 time-series was the first obtained in the Eastern Mediterranean. The study was conducted at a fixed platform of the POSEIDON system (Heraklion Coastal Buoy) located near Crete Island. Temperature was the dominant factor controlling the temporal variability of pCO2 and pH, while the remaining non-thermal variability appeared to be related to evaporation, water mixing, and biological remineralization-production. The air-sea CO2 fluxes indicated a transition from a winter-spring sink period to a summer-autumn source period. The annual air-sea CO2 flux was too low (-0.16 ± 0.02 mol m-2 yr-1) and variable to conclusively characterize the area as a net source or sink of CO2, highlighting the need for additional high frequency observation sites. Algorithms were developed using temperature, chlorophyll and salinity data to estimate pCO2 and total alkalinity, in an effort to provide tools for estimates in poorly observed areas/periods from remotely sensed products. The applicability of the algorithms was tested using Surface Ocean CO2 Atlas (SOCAT) data from the Eastern Mediterranean Sea (1999 to 2020) which showed that the algorithm pCO2 estimates were generally within ±20 μatm of the pCO2 values reported by SOCAT. Finally, the integration and analysis of the data provided directions on how to optimize the observing strategy, by readapting sensor location and using estimation algorithms with remote sensing data.

Assessment of the Spatiotemporal Variability of Urban Heat Island Intensity in Colombo Metropolitan Area

&#x0D; &#x0D; &#x0D; During the last few decades, the Colombo Metropolitan Area (CMA) has experienced rapid population growth and unplanned urbanization, resulting in the replacement of natural landcover with more impervious surfaces. The primary approach of current study was to determine the gradients of air temperature and assess the spatiotemporal variation of atmospheric urban heat island (AUHI) intensity in the CMA considering a range of rural sites extending in different radial directions from the urban center. A total of sixteen rural observation sites that extend from an urban center in four directions were selected within the CMA, with four sites 3.5 km apart from each other in any direction. The air temperatures at each site were measured using HOBO UX-100-003 temperature data loggers for an 8-day period from 21st to 28th, of February 2022. The normalized difference vegetation index (NDVI) and normalized difference building index (NDBI) were used to determine the influence of land use/land cover on air temperatures. The results revealed that air temperature had a strong positive correlation with NDBI. Thus, the current study suggests that the impervious surface/s is a significant predictor of air temperature over the study area. The higher air temperatures were observed at the urban center in relation to the selected suburban and rural observation sites. Hence, the current study confirms that AUHI exists within the selected study area over the CMA. It was found that AUHI tends to be present throughout the day in the CMA. However, it becomes strong between 12.00 A.M. and 6.00 A.M and mostly weakens in the late afternoon hours. Furthermore, this study found that the timing and magnitudes of AUHI vary from direction to direction across the study area depending on the site-specific characteristics of the rural observation sites. The current study emphasizes that site-specific land use/land cover characteristics can have a significant impact on the interpretation of UHI study results even within a single metropolitan area. Therefore, taking only one rural site might not be effective in determining the UHI intensity and a range of sites need to be considered (to select from) for in-depth analysis of UHI in any given area. The findings of the current study will provide firsthand knowledge for urban planners to identify future trends in UHI in the CMA and will promote further UHI research in this area.&#x0D; Keywords: Rural observation site, Atmospheric urban heat island, Normalized Difference Vegetation Index&#x0D; &#x0D; &#x0D;

Molecular characterization of Rice ragged stunt virus and Rice grassy stunt virus on Rice in Gianyar, Bali, Indonesia

Rice stunt disease is one of the causes of rice harvest failure. It is caused by the infection of Rice ragged stunt virus (RRSV) and Rice grassy stunt virus (RGSV) infection. Information about disease severity and the molecular characteristics of stunt viruses in Indonesia is still limited. Thus, this research aimed to determine the disease severity and the genetic diversity of rice stunt viruses in Gianyar, Bali. The research method consisted of observation of incidence and disease severity in the field and virus detection by reverse transcription-polymerase chain reaction (RT-PCR) using primers specific for RRSV and RGSV. The observation of the disease incidence and severity were performed in seven districts in Gianyar Regency, Bali, namely Blahbatuh, Gianyar, Payangan, Sukawati, Tampaksiring, Tegallalang, and Ubud. Stunt disease was found in all observation sites. High stunt disease incidence (&gt; 44%) was found in three districts: Ubud, Tampaksiring, and Payangan, while the low disease incidence rate of &lt;10% was found in Blahbatuh and Gianyar Districts. The highest stunt disease severity occurred in Tampaksiring District (60.82%), while the lowest severity occurred in Gianyar District (18.84%). The IR-64 and Ciherang cultivars are vulnerable to rice stunt disease infection. The highest homology of RRSV and RRGV nucleotides was found with Vietnam isolates being &gt;98% and &gt;97%, respectively. The phylogenetic analysis showed that Indonesian isolates of RRSV and RGSV were clustered in the same group as Vietnam isolates.

Retrieval of Surface Energy Fluxes Considering Vegetation Changes and Aerosol Effects

The exchange of moisture and energy between the land and the atmosphere plays a crucial role in terrestrial hydrological cycle and climate change. However, existing studies on the retrieval of surface water and heat flux tend to overlook the dynamic changes in surface vegetation and atmospheric aerosols, which directly affect surface energy and indirectly alter various meteorological factors, including cloud, precipitation, and temperature. In this study, we assess the machine-learning retrieval method for surface fluxes that takes into account vegetation changes and aerosol effects, using FLUXNET observations and remote sensing data to retrieve latent heat flux (LE) and sensible heat flux (H). We constructed four sets of deep neural network models: (a) The first set considers only meteorological factors, (b) the second set considers meteorological factors and aerosols, (c) the third set considers meteorological factors and vegetation changes, and (d) the fourth set comprehensively considers meteorological factors, aerosols, and vegetation changes. All model performances were evaluated using statistical indicators. ERA5 reanalysis and remote sensing data were used to drive the models and retrieve daily H and LE. The retrieved results were validated against ground observation sites that were not involved in model training or the FLUXCOM product. The results show that the model that considers meteorological factors, aerosols, and vegetation changes has the smallest errors and highest correlation for retrieving H and LE (RH = 0.85, RMSEH = 24.88; RLE = 0.88, RMSELE = 22.25). The ability of the four models varies under different vegetation types. In terms of seasons, the models that consider meteorological factors and vegetation changes, as well as those that comprehensively consider meteorological factors, aerosols, and vegetation changes, perform well in retrieving the surface fluxes. As for spatial distribution, when atmospheric aerosols are present in the region, the model that considers both meteorological factors and aerosols retrieves higher values of H compared to the model that considers only meteorological factors, while the LE values are relatively lower. The model that considers meteorological factors and vegetation changes, as well as the model that comprehensively considers meteorological factors, aerosols, and vegetation changes, retrieves lower values in most regions. Through the validation of independent observation sites and FLUXCOM products, we found that the model, considering meteorological factors, aerosols, and vegetation changes, was generally more accurate in the retrieval of surface fluxes. This study contributes to improving the retrieval and future prediction accuracy of surface fluxes in a changing environment.

Studying the mass sensitivity of air-shower observables using simulated cosmic rays

Using simulations, we investigate the mass sensitivity of cosmic-ray air-shower observables for sites at the South Pole and Malargüe, Argentina, the respective locations of the IceCube Neutrino Observatory and the Pierre Auger Observatory. Exact knowledge of observables from air-shower simulations was used to study the event-by-event mass separation between proton, helium, oxygen, and iron primary cosmic rays with a Fisher linear discriminant analysis. Dependencies on the observation site as well as the energy and zenith angle of the primary particle were studied in the ranges from 1016.0–1018.5 eV and 0° to 60°; they are mostly weak and do not change the qualitative results. Promising proton-iron mass separation is achieved using combined knowledge of all studied observables, also when typical reconstruction uncertainties are accounted for. However, even with exact measurements, event-by-event separation of intermediate-mass nuclei is challenging and better methods than the Fisher discriminant and/or the inclusion of additional observables will be needed. As an individual observable, high-energy muons (&gt;500 GeV) provide the best event-by-event mass discrimination, but the combination of muons of any energy and Xmax provides already a high event-by-event separation between proton-iron primaries at both sites. We also confirm that the asymmetry and width parameters of the air-shower longitudinal profile, R and L, are mass sensitive. Only R seems to be suitable for event-by-event mass separation, but L can potentially be used to statistically determine the proton-helium ratio. Overall, our results motivate the coincident measurement of several air-shower observables, including at least Xmax and the sizes of the muonic and electromagnetic shower components, for the next generation of air-shower experiments. Published by the American Physical Society 2024

Characteristics of Soil Moisture and Heat Change during Freeze–Thaw Process in the Alpine Grassland of Duogerong Basin in the Source of the Yellow River

To deeply understand the characteristics of soil freeze–thaw water–heat change in the alpine grassland in the Duogerong Basin of the Yellow River source, the soil water–heat profile change monitoring was carried out based on the field monitoring station in the Duogerong Basin of the Yellow River source. By analyzing the comprehensive monitoring data from September 2022 to September 2023, the characteristics of the soil temperature and water content changes in the freeze–thaw cycle of the alpine grassland in the Duogerong Basin at the source of the Yellow River were explored. The results showed that the temperature and water content of each layer of the soil profile changed periodically, and the range of change was negatively correlated with the depth. The annual freeze–thaw process at the observation site is divided into five stages: 31 October to 3 November is the short initial freezing period, 4 November to 18 April is the stable freezing period, 19 April to 26 April is the early ablation period, 27 April to 30 April is the late ablation period, and 1 May to 30 October is the complete ablation period. The maximum soil freezing depth during the observation period was about 250 cm. Soil temperature and moisture content change affect each other; soil water is essential in heat transfer, and the two correlate well. The research results provide theoretical support for further understanding the characteristics of soil hydrothermal changes during the freeze–thaw process in the alpine grassland permafrost area at the source of the Yellow River.