Continuous Monitoring Stations Research Articles

The Prospect of Typhoon Landfall Observation Utilizing L-band InSAR

The cyclonic phenomenon, particularly typhoons within the 100° E to 180° E longitude region, bring significant devastation through intense rainfall and strong winds, causing secondary effects like landslides and floods upon landfall. This results in infrastructural damage and loss of life, ranking storm-related disasters, including typhoons, second only to earthquakes in casualties, emphasizing the need for a robust observation system. While higher income countries benefit from established radar and meteorological stations for continuous monitoring, this study highlights the critical necessity for an additional monitoring system in lower income countries. We investigate the applicability of Interferometric Synthetic Aperture Radar (InSAR) during typhoon landfall, particularly in densely vegetated areas where longer wavelengths like L-band (for instance, the Advanced Land Observing Satellite (ALOS/PALSAR) and ALOS-2/PALSAR-2) are suitable. By analyzing typhoon occurrences and confirming landfall using weather radar from 2007 to 2011 (ALOS/PALSAR observation period) and 2014 to 2023 (ALOS-2/PALSAR-2 observation period), our study successfully identifies the landfall of the Jebi typhoon on September 4, 2018. InSAR processing reveals significant phase gradients linked to dense water vapor during landfall, corroborated by comparisons with sea-level Zenith Wet Delay (ZWD) from Global Navigation Satellite System (GNSS) data and hourly precipitation rates from the Automated Meteorological Data Acquisition System (AMeDAS). The concurrence of InSAR results with ZWD values and rainfall data from meteorological stations validates the potential of L-band InSAR in observing typhoon landfalls.

Impacts of anthropogenic emissions and meteorology on spring ozone differences in San Antonio, Texas between 2017 and 2021

San Antonio has been designated as ozone nonattainment under the current National Ambient Air Quality Standards (NAAQS). Ozone events in the city typically occur in two peaks, characterized by a pronounced spring peak followed by a late summer peak. Despite higher ozone levels, the spring peak has received less attention than the summer peak. To address this research gap, we used the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC) model to simulate San Antonio's ozone changes in the spring month of May from 2017 to 2021 and quantified the respective contributions from changes in anthropogenic emissions and meteorology. In addition to modeling, observations from the San Antonio Field Studies (SAFS), the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Stations (CAMS), and the spaceborne TROPOspheric Monitoring Instrument (TROPOMI) are used to examine and validate changes in ozone and precursors. Results show that the simulated daytime mean surface ozone in May 2021 is 3.8 ± 0.6 ppbv lower than in May 2017, which is slightly less than the observed average differences of −5.3 ppbv at CAMS sites. The model predicted that the anthropogenic emission-induced changes contribute to a 1.4 ± 0.5 ppbv reduction in daytime ozone levels, while the meteorology-induced changes account for a 2.4 ± 0.6 ppbv reduction over 2017–2021. This suggests that meteorology plays a relatively more important role than anthropogenic emissions in explaining the spring ozone differences between the two years. We additionally identified (1) reduced NO2 and HCHO concentrations as chemical reasons, and (2) lower temperature, higher humidity, increased wind speed, and a stronger Bermuda High as meteorological reasons for lower ozone levels in 2021 compared to 2017. The quantification of the different roles of meteorology and ozone precursor concentrations helps understand the cause and variation of ozone changes in San Antonio over recent years.

The effect of various air pollution and participants' age on semen quality in southern Taiwan

ObjectiveThis study aimed to investigate the association between semen quality and air pollution in southern Taiwan. Materials and methodsIn this retrospective study, 4338 males aged 21–70 years were recruited between 2001 and 2018 from a reproductive medical center. Semen quality was assessed according to standardized methods outlined in the World Health Organization (WHO) Laboratory Manual 1999 and 2010, including total sperm count, progressive sperm motility (%), rapid progressive sperm motility (%), and sperm with normal morphology (%). All designated national air quality automatic continuous monitoring stations measured the levels of air pollution [particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3)], and was documented by Environmental Protection Administration in Taiwan. We collected data on the levels of air pollution based on the participants' residential addresses. ResultsIn our study, we found that progressive and rapid progressive sperm motility significantly decreased annually (p < 0.05). In addition, increasing age influenced total sperm count, progressive sperm motility, rapid progressive sperm motility, and sperm with normal morphology (p < 0.05). Among different air pollution, we observed SO2 was associated with lower rapid progressive sperm motility and lower sperm with normal morphology (β = −0.103, p = 0.043; β = 0.118, p = 0.001, respectively). However, NO2 was associated with higher rapid progressive sperm motility and a high number of sperm with normal morphology (β = 0.129, p = 0.002; β = 0.127, p < 0.001, respectively). ConclusionsThe semen quality in southern Taiwan appears to have declined in recent years. The participant's age for semen analysis was most strongly associated with semen parameters, Moreover, a significant association between SO2 and NO2 levels and semen motility was observed, even after adjusting for multiple comparisons. Further study is required to analyze the dose-dependent effect of SO2 and NO2 on semen parameters.

Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas

Abstract. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R&gt;0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (&lt;50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.

МОНИТОРИНГ АТМОСФЕРНЫХ ЗАГРЯЗНЕНИЙ В ГОРОДСКИХ И СЕЛЬСКИХ РАЙОНАХ БАЙКАЛЬСКОЙ ПРИРОДНОЙ ТЕРРИТОРИИ ЗА 2019-2021 ГГ

The spatiotemporal distribution of concentrations of trace gases (SO2, NOx, and CO) in the atmospheric surface layer over different areas of the Baikal natural territory is analyzed using the data from seven continuous monitoring stations for 2019-2021. The most contaminated area is the zone of atmospheric influence in the western and northwestern directions from the lake (the cities of Irkutsk, Angarsk, Usol’e-Sibirskoe, Cheremkhovo). The purest area is the central zone of the Baikal natural territory. Changes in atmospheric concentrations of pollutants due to wildfires and measures taken during the COVID-19 pandemic are shown.

Application of Deep Learning to Enforce Environmental Noise Regulation in an Urban Setting

Reducing environmental noise in urban settings, i.e., unwanted or harmful outdoor sounds produced by human activity, has become an important issue in recent years. Most countries have established regulations that set maximum permitted noise levels. However, enforcing these regulations effectively remains challenging as it requires active monitoring networks and audio analysis performed by trained specialists. The manual evaluation of the audio recordings is laborious, time-consuming, and inefficient since many audios exceeding the noise level threshold do not correspond to a sound event considered by the regulation. To address this challenge, this work proposes a computational pipeline to assist specialists in detecting noise sources in the built environment that do not comply with the Chilean noise regulation. The system incorporates a deep neural model following a pre-trained audio neural network architecture transferred to a dataset compiled from public sources and recordings in Valdivia, Chile. The target dataset follows a customized taxonomy of urban sound events. The system also uses a public API so potential users can post audio files to obtain a prediction matrix reporting the presence of noise sources contributing to environmental noise pollution. Experiments using recordings from two continuous noise monitoring stations showed that the amount of data to be inspected by the specialist is decreased by 97% when the deep-learning tools are used. Therefore, this system efficiently assists trained experts in enforcing noise legislation through machine-assisted environmental noise monitoring.

A Review on Noise Pollution Monitoring, Mapping, Modelling, and Health Impacts

Most people are not aware that noise from road traffic is the principal cause of annoyance to exposed people. In relation to this, the review aims at providing an overview of research carried out on noise pollution monitoring, mapping, modelling, and health impacts in recent years in different countries. The electronic databases were searched to select the studies related to noise monitoring, mapping, modelling, and health impacts on humans. A total of 91 most relevant studies were reviewed in this article. A comparison of different instruments available for noise monitoring was done. Studies related to monitoring and mapping reveal that most of the locations have higher noise levels in the environment, breaching the noise limits. Only a few articles enumerated health impacts and zone-specific monitoring. Many researchers have conducted a questionnaire-based study related to health to identify the impact of noise. Noise models for many developing countries are not available, so their development and modification are still in progress. Proper zone-specific studies and noise maps will help the town planner with better land use planning. Continuous monitoring stations for noise in crowded cities shall be implemented to mitigate noise pollution. Based on the review, it is concluded that long-term research on noise pollution is very much needed worldwide.

UAVs and GIS: An Innovative System for Monitoring Structures

In the last years, Unmanned Aerial Vehicle (UAV) – systems have become relevant for multiple purposes in different fields of study. The purpose of this work is presenting an innovative experimental system for the assessment of the structural conditions of buildings and infrastructures through continuous monitoring stations based on the use of UAVs. This allows the automation in the monitoring of artifacts that present structural criticalities. The system consists in the acquisition, processing, and selection of images from a group of drones flying from wireless charging stations, whose data are then transmitted to a cloud platform which identifies defects and transmits them to an online Web-GIS platform. This will be the tool for viewing all elements that need maintenance. In addition to the improvement in the automation of data processing and in the precision of their selection, we will analyze an experimental procedure that uses different algorithms for damage detection.

Geochemistry and fluxes of gases from hydrothermal features at Newberry Volcano, Oregon, USA

We present the chemical and isotopic compositions of gases and fluxes of CO2 from the hydrothermal features of Newberry Volcano, a large composite volcano located in Oregon's Cascade Range with a summit caldera that hosts two lakes, Paulina and East Lakes. Gas samples were collected from 1982 to 2021 from Paulina Hot Springs (PHS) on the shore of Paulina Lake, East Lake Hot Springs (ELHS) on the shore of East Lake, and Obsidian Flow Gas Seep (OFGS), an area of diffuse gas emissions. Surveys of CO2 flux were conducted in 2020 at OFGS (1400 m2) and East Lake (4.1 km2). Gases from all three sites were CO2-rich (≥79 mol% in dry gas) but showed considerable compositional variability over time due to interaction with ground and surface water. An increase in H2S concentrations and decline in CO2/H2S ratios in ELHS gases coincided with a drop in East Lake water level from 1999 to 2021. ELHS and OFGS gases were high in CH4 relative to PHS and the δ13C of CH4 values for ELHS gases (−72.2 and − 63.6 ‰) reflected a predominantly biogenic origin. The dominant source of N2 and Ar in PHS, ELHS, and OFGS samples was likely groundwater. Helium isotopic ratios (6.47 to 8.02 Rc/Ra) support a persistent source of magmatic He beneath Newberry caldera and consistently high values measured at OFGS and PHS relative to ELHS suggest distinct fluid flow paths from depth to the surface features. The δ13C of CO2 and CO2/3He values (−8.9 to −5.35 ‰ and 1.3 × 109 to 4.6 × 1010, respectively) measured in gases reflect contributions of CO2 from both mantle and crustal sources. Measured CO2 fluxes at OFGS and East Lake ranged from 1 to 8808 and < 1 to 364 g m−2 d−1, respectively. A CO2 emission rate of 0.5 t d−1 was calculated for OFGS. The CO2 emission rate estimated for East Lake was 30 t d−1 and when compared to prior estimates, reflects steady-state lake degassing. An enhanced geochemical monitoring plan, including annual sampling of gases at ELHS, OFGS, and PHS for geochemical analysis, installation of a continuous lake-level monitoring station at East Lake, and annual CO2 flux surveys at OFGS, would provide valuable background data and insights into any precursor volcanic activity. Integrating geochemical data with data from the real-time seismic and GPS network at Newberry Volcano could better resolve and interpret potential changes in its magma-hydrothermal system.

Using Low-Cost Sensors to Assess PM2.5 Concentrations at Four South Texan Cities on the U.S.—Mexico Border

Low-cost sensors have been used considerably to characterize air pollution in the last few years. This study involves the usage of this technology for the first time to assess PM2.5 pollution at four cities on the U.S.–Mexico border. These cities in the Lower Rio Grande Valley Region of South Texas are Brownsville, Edinburg, Weslaco, and Port Isabel. A year-long sampling campaign was undertaken from 1 March 2021 to 31 March 2022. TSI BlueSky™ Air Quality Monitors were deployed concurrently at 11 different locations in these four cities. Twenty-four-hour PM2.5 concentrations from these sensors were then compared with ambient PM2.5 data available at the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Station (CAMS) sites to elucidate spatial and temporal variability in the pollutant concentrations at the neighborhood level. The results indicate low to moderate spatial heterogeneity in the PM2.5 concentrations throughout the region. Our findings suggest that low-cost sensors in combination with CAMS sites have the potential to aid community monitoring for real-time spatiotemporal PM2.5 pollution patterns.

Impact of COVID-19 lockdown and meteorology on the air quality of Srinagar city: A temperate climatic region in Kashmir Himalayas

The deadly transmission of the coronavirus forced all countries to implement lockdowns to restrict the transmission of this highly infectious disease. As a result of these lockdowns and restrictions, many urban centers have seen a positive impact on air quality with a significant reduction in air pollution. Therefore, in this study, the impact of COVID-19 lockdown vis-a-vis meteorological parameters on the ambient air quality of Srinagar city was examined. In this regard, we have evaluated the temporal variation of six different key air pollutants (PM10, PM2.5, SO2, NO2, O3, and NH3) along with meteorological parameters (relative humidity, rainfall, temperature, wind speed, and wind direction). The duration of the study was divided into three periods: Before Lockdown(BLD), Lockdown (LD), and Partial Lockdown(PLD). Daily average data for all the parameters was accessed from one of the real-time continuous monitoring stations of the central pollution control board (CPCB) at Rajbagh Srinagar. Some air pollutants have decreased, according to the results, while others have increased. The air quality index (AQI) decreases overall by 6.15 percent compared to before lockdown, and it never exceeds the "moderate" category. The AQI was in the following order for both lockdown and pre-lockdown periods: satisfactory > moderate > good. However, for partial lockdown, it was moderate > satisfactory > good. It was observed that the maximum decrease was seen in the concentration of NO2, NH3 with 75.11% and 69.18%. A modest decrease was observed in PM10 at 3.8%. While SO2 and O3 had an upward trend of 85.82% and 48.74%, The NO2 to SO2 ratio reveals that the emissions of NO2 have substantially decreased due to the complete restriction of transport systems. From principal component analysis for all three study periods, PM10 and PM2.5 were combined into a single component, inferring their shared behavior and source of origin. SO2 and O3 demonstrated identical behavior during the lockdown and partial lockdown periods of study. According to the findings of the study, it is beneficial for the government, environmentalists, and policymakers to impose rigorous lockdown measures, particularly during extreme air pollution events, in order to reduce the damage caused by automotive and industrial emissions.

Comparison of Ambient Air Quality among Industrial and Residential Areas of a Typical South Asian City

The rapid increase in population growth due to industrialization and urbanization has resulted in air quality deterioration in Pakistan. Consequently, a considerable increase has been seen in the types of sources of air pollutants. However, the air quality of the country has deteriorated in the absence of management capabilities against air quality. Evidence from numerous governmental organizations and international bodies has specified that the environment, health, and quality of life are at high risk due to air pollution. Although the government of Pakistan established the Pakistan Clean Air Program, along with continuous monitoring stations to manage the quality of ambient air, air quality values have not yet been achieved. The present investigations were made in the city of Faisalabad in selected locations. Sampling of a 24 h average was done for selected sites. The air quality parameters such as NO2, SO2, COx, O2, noise level, and suspended particulate matter (SPM) were measured at two locations, i.e., Khurrianwala and Liaqatabad in the Faisalabad District. The measured values of air quality parameters were compared with national environmental quality standards (NEQS). Air pollutants such as SPM, SO2, and noise levels were found to be significantly higher than the 24-h standards of NEQS, which poses harmful effects on the quality of air and health, whereas the O2 concentration was found to be lower than the normal values, and NO2 and COx values were normal. The SO2, CO2, noise level, SPM, and O2 values ranged from 418–652 and 423–661 µg/m3, 3.03–3.44 and 3.08–3.51 mg/m3, 68–73 and 69–75 dB, 555–667 and 581–682 µg/m3, and 19.5–20 and 19.5–20.3 % for summer and winter season, respectively, as compared to standard values (150 µg/m3, 10 mg/m3, 65 dB, 550 µg/m3 and 21%). After the complete analysis of the selected locations, it was concluded that the ambient air quality of this area is severely degraded due to industrial as well as other commercial activities. These significant variations in air quality parameters suggest that there is a need to check the air quality regularly to take appropriate measures for reducing ambient air pollutants, especially in industrial areas as well as commercial areas.

Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data

Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis. One particular data imputation challenge is filling large data gaps when the only related data comes from the same sensor station. In this paper, we propose a framework to improve the popular multivariate imputation by chained equations (MICE) method for dealing with missing data. One key strategy we use to improve model accuracy is to reshape the original sensor data to leverage the correlation between the missing data and the observed data. We demonstrate our framework using data from continuous water quality monitoring stations in Vermont. Because of possible irregularly spaced peaks throughout the time series, the reshaped data is split into extreme and normal values and two MICE models are built. We also recommend that sensor-collected data should be transformed to meet the machine learning model assumptions. According to our experimental results, these strategies can improve MICE data imputation model accuracy at least 23% for large data gaps based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {R}^{{2}}$ </tex-math></inline-formula> values and are promising to be applied for other data imputation algorithms.

Evaluation of the SWAT Model With Respect to Different Variables Within the Tomebamba River Sub-Basin Belonging to the Paute River Basin, Ecuador

The objective of this study is to evaluate the efficiency of the SWAT model with respect to simulations of nitrates, organic phosphorus, dissolved oxygen, sediments and evapotranspiration, taking as reference data observed in continuous monitoring stations. The results showed that the correlation between the simulated and observed data is practically null if a linear relationship were established between them (Pearson correlation coefficient). On the other hand, the simulations overestimated the values of evapotranspiration, dissolved oxygen and sediments; and underestimated the values of nitrates and organic phosphorus, according to the PBIAS%. The RSR statistical criterion indicates that the performance of the modeling is unsatisfactory for nitrates, evapotranspiration and dissolved oxygen; and it is very good for organic phosphorus and the sediments of the Llaviuco station, which was verified with the application of the NSE statistical criterion. It is very important to pay attention to the initial calibration of the model.

A Comprehensive Evaluation of Gridded L-, C-, and X-Band Microwave Soil Moisture Product over the CZO in the Central Ganga Plains, India

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman’s rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters.

Prediction of COVID-19 Cases from the Nexus of Air Quality and Meteorological Phenomena: Bangladesh Perspective

An integrated approach was used to estimate the number of COVID-19 patients related to air quality and meteorological phenomena. Additionally, the air quality during pre-lockdown, lockdown, and post-lockdown stages of the COVID-19 pandemic was assessed to determine the effect of the infection containment measures taken in Bangladesh during the pandemic. The air quality was assessed based on measurements of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), black carbon, particulate matter (PM2.5 and PM10), and aerosol optical depth. Time-averaged maps of these parameters have been generated from NASA’s (National Aeronautics and Space Administration) website. Values of these parameters have also been collected from a continuous air monitoring station (CAMS) located in Bangladesh’s north-western city Rajshahi. The comparison shows that lockdown during the pandemic has brought significant improvements in air quality. However, the improvement was not sustained, since rapid increases in the air pollutant concentrations were observed in the post-lockdown period. Furthermore, Pearson correlation coefficients between each air quality variable and the daily new COVID-19 case rates were calculated. Different meteorological variables during the same time periods were determined to observe the variation in Rajshahi city. Relationships of these variables with the case rates were also established. Additionally, statistical analyses of the obtained data have been conducted for the measured variables using the Kruskal–Wallis test to assess the differences in the observed data among the pre-lockdown, lockdown, and post-lockdown periods. Dunn’s “Q” test was employed to test if the variables showed significance statistical difference during the Kruskal–Wallis test for pairwise comparisons. From the study, it has been observed that both meteorological variables and air quality parameters have significant relationship with daily new COVID-19 case rates. Both positive and negative associations of these parameters with the COVID-19 case rates have been observed. Very high air pollution has been observed in the post-lockdown period. Thus, it is recommended that appropriate authorities undertake corrective measures to protect the environment in cities with large populations. This study provides guidance for decision makers and health officials for future research and potentially reducing the spread of COVID-19.

Groundwater trends during 1985 to 2019 in a critical groundwater area of northeastern Arkansas

The Mississippi River Valley alluvial aquifer provides over 90% of irrigation water for agriculture in the Lower Mississippi River Basin. Overexploitation of the alluvial aquifer in the Cache River Critical Groundwater Area (CRCGA) in Arkansas started impacting crop production in the 2000s with previous historical data identifying sustained groundwater declines. Groundwater depletion resulted in many producers investing in on-farm reservoir-tailwater recovery systems to provide a more secure irrigation source. More focused and consistent water level measurements were necessary to identify any groundwater level changes. Groundwater trends focused on the CRCGA cone of depression were compared from two-time groupings: 1985 to 2012 and 2012 to 2019. On-farm reservoirs in the CRCGA were inventoried in 2015, and 90% of those inventoried were constructed by 2012. More frequent groundwater level data collection in the CRCGA started in 2012 as well. Groundwater levels during 1985 to 2012 had consistent decline rates of –0.17 to –0.44 m y^–1. Groundwater levels during 2012 to 2019 varied with decline rates of ≤–0.10 to ≤–0.50 m y^–1, but also areas of no significant change and recovery at rates of ≤0 to ≤0.25 m y^–1. Trend differences between the two periods showed improved water level trends for most of the study area, with rates ranging ≤–0.30 to ≤0.50 m y^–1. The measured improvements were attributed to less pumping due to greater precipitation and the use of surface water irrigation from 60 on-farm reservoir-tailwater recovery systems. Detection of these improvements is crucial to understanding the response and status of the alluvial aquifer within the agricultural area of the CRCGA. It is recommended that adding continuous groundwater monitoring stations in west Craighead and Poinsett counties would be beneficial to inform the status of the cone of depression, considering the importance of protecting the main agricultural irrigation supply for the state. If warmer temperatures occur in the future, higher temporal and spatial groundwater level data in the CRCGA will allow policymakers and water resource managers to make timely groundwater management decisions to ensure its supply for future generations.

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

AbstractSoil climate, as quantified by soil temperature (TS) and water content (θ), exerts important controls on critical zone processes. It may be sensitive to variations in local slope and aspect (SA), but this attribute remains poorly quantified at the local scale and unresolved in large‐scale models. Estimation of SA effects on soil climate across multiple scales may facilitated using topographically modified, incoming clear‐sky solar radiation (SR,CS,T). We established six paired automated soil climate monitoring stations on opposing north‐facing (NF) and south‐facing (SF) slopes (4 yr) and collected spatialTSand θ data within the hectare surrounding four stations (2 yr) to measure SA effects on soil climate. Results were compared with physically based simulations and evaluated in the context ofSR,CS,T. Spatial θ data were more variable thanTs, and both were consistent with values from continuous monitoring stations. On average, the SFTSwas much greater (4.7 °C) and the annual summer drought longer (36 d) than on the adjacent NF aspect. Seasonal variations ofTSand θ were different from each other and also different fromSR,CS,T. Local conditions, including snow cover, precipitation patterns, and soil properties, largely controlled seasonal variations ofTSand θ, which cannot be predicted fromSR,CS,T. This indicates that realistic simulation of many critical zone processes requires high‐resolution inputs. Simulations captured first‐order SA effects and could be useful for estimating SA effects in lieu of field monitoring.

Monitoring Turbidity in San Francisco Estuary and Sacramento-San Joaquin Delta Using Satellite Remote Sensing.

This study utilizes satellite data to investigate water quality conditions in the San Francisco Estuary and its upstream delta, the Sacramento–San Joaquin River Delta. To do this, this study derives turbidity from the European Space Agency satellite Sentinel‐2 acquired from September 2015 to June 2019 and conducts a rigorous validation with in situ measurements of turbidity from optical sensors at continuous monitoring stations. This validation includes 965 matchup comparisons between satellite and in situ sensor data across 22 stations, yielding R 2 = 0.63 and 0.75 for Nephelometric Turbidity Unit and Formazin Nephelometric Unit (FNU) stations, respectively. This study then applies remote sensing to evaluate patterns in turbidity during the Suisun Marsh Salinity Control Gates Action (“Gates action”), a pilot study designed to increase habitat access and quality for the endangered Delta Smelt. The basic strategy was to direct more freshwater into Suisun Marsh, creating more low salinity habitat that would then have higher (and more suitable) turbidity than upstream river channels. For all seven acquisitions considered from June 29 to September 27, 2018, turbidity conditions in Bays and Sloughs subregions were consistently higher (and more suitable) (26–47 FNU) than what was observed in the upstream River region (13–25 FNU). This overall pattern was observed when comparing images acquired during similar tidal stages and heights.

Impact of the noise indicators components correlation [formula omitted] on the uncertainty of the long-term day–evening–night noise indicator [formula omitted

The paper studies the actual impact of the noise indicators components on the Lden uncertainty. Various sampling methods in order to correctly determine the Lden uncertainty depending on the measurement sample size were also analysed. The analysis was conducted on the actual measurement data from three continuous noise monitoring stations in Gdańsk, Poland. The statistical inference was based on the results of t-test at significance level γ=0.05. The analysed data were comprehensive and differentiated and covered 3 years. The analysis indicated that the 95% intervals determined with the use of independent draw are narrower than the intervals calculated using dependent sampling. For small samples, n=5, the uncertainty evaluation method using independent draw without correlation has the highest coverage degree of actual value, and for samples n={10,15} it is the method using dependent draw with correlation. This method has also the highest coverage regardless of the acoustic signal variability.