Spatio-Temporal Evolution and Driving Factors of "Non-Grain Production” in Hubei Province Based on a Non-Grain Index

Non-grain production (NGP) on cultivated land has become a common phenomenon due to the prosperity of the rural economy and the optimisation of the agricultural structure. However, the excessive use of cultivating land for NGP has threatened food production and the sustainable use of cultivated land. To halt this trend and to ensure food security, the authors of this paper applied a novel non-grain index to measure NGP, which could reflect multiple NGP activities;designated Hubei Province as its object of research;and revealed NGP's spatio-temporal patterns of the past 30 years. We then assessed the characteristics of NGP based on spatial autocorrelation analysis, the Theil index, and geographically weighted regression. The results showed that the value of the non-grain index grew from 0.497 to 1.113 as NGP increased significantly in Hubei Province. The number of high-NGP counties increased, spatial agglomeration became obvious, and the eastern and western sides of Hubei Province witnessed an observable growth in NGP. As a result, the NGP in the eastern and western regions overtook production in the central region. Despite a series of historical subsidy policies and agricultural modernisation initiatives that promoted the planting of grain crops, the policy of "grain on valuable cultivated land” could be better implemented. We conclude by making some suggestions for reducing NGP and protecting cultivated land.

Diagnostic Analysis of mZN Microscopy and PCR for Detection of Cryptosporidiosis in Goat Kids - A Molecular Epidemiological Study

The occurrence of Cryptosporidium species infection and its risk factors in neonatal goats is less explored. Also it is due to the fact that diseases like colibacillosis and neonatal viral enteritis complex caused by Group A rotaviruses and Bovine corona viruses can co-exist with Cryptosporidium and can lead to mixed infections and the latter is often overlooked. Therefore, in the current research we explored the cryptosporidial occurrence in neonatal goats of Mathura district of Uttar Pradesh, India. In this study, a total of 644 faecal samples were collected from neonatal goats at different villages and certain organized farms of Mathura district age-wise, season-wise and breed-wise, and were examined for Cryptosporidium based on modified Ziehl-Neelsen technique, conventional 18SSU rRNA nested PCR assay. The overall prevalence of Cryptosporidium infection in goats based on microscopy was 36.80% (237/644;p value <0.0001) and 18SSU rRNA nested PCR 52.95% (341/644;p value <0.0001) respectively. Cryptosporidium species typing was also done using 18SSU rRNA nested PCR-RFLP product using enzymes Mbo-II, Ssp-I and Vsp-I, which revealed species including C. parvum C. bovis, C. ryanae, C. hominis and C. andersoni. Also the infection was clinically associated based on age, gender and seasons to identify the causal relationships that precipitate the cryptosporidial infection in goat kids. Since mZN microscopy based screening requires expertise and may sometimes be confuse with other weak acid fast bodies and also due to low sensitivity, combination of diagnostic tests are used in this study to identify the best test combination that yields best statistical fit in terms of kappa-agreement and McNemar's test. Cryptosporidiosis is caused by an enteric protozoan parasite and the first report in sheep and goat was observed in early 1980s, with other important etiological agents for neonatal diarrhoea, mortality and morbidity in neonatal kids and lambs, responsible for economic losses.Copyright © 2023 Indian Veterinary Assocaition. All rights reserved.

Sustainability assessment of crops' production in India: empirical evidence from ARDL-ECM approach

PurposeThe study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.Design/methodology/approachThe study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.FindingsThe ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.Research limitations/implicationsThe study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.Originality/valueFor a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

New-Onset Thyroid Eye Disease After COVID-19 Infection in Patient with Graves' Disease Post-Radioactive Iodine Treatment

Introduction: Coronavirus disease 2019 (COVID-19) has been associated with dysregulation of the immune system and abnormal thyroid function. The aim of this novel case report is to inform physicians of the possibility that COVID-19 infection may precipitate thyroid eye disease (TED) in patients with Graves' Disease (GD) even after treatment with radioactive iodine (RAI). Case Description: In this report, we describe a patient with GD treated with RAI who developed TED after COVID-19 infection. The patient was initially diagnosed with GD in 2018. A thyroid uptake scan (I-123) was consistent with GD with moderately elevated uptake. She was initially managed with methimazole and atenolol and was eventually treated with RAI (16.32 millicurie I-131) in February 2021. She had post-ablative hypothyroidism managed with levothyroxine. The patient contracted COVID-19 in January 2022. In February 2022, the patient started experiencing eye irritation, dryness, protrusion of eyes, eyelid swelling, and visual disturbances. Thyroid stimulating hormone (TSH) receptor auto-antibodies (7.33 IU/L, normal < /=1.00 IU/L) and thyroid stimulating immunoglobulin (4.30 IU/L, normal < /=1.00 IU/L) were elevated. TSH was normal (2.180 mIU/L, normal 0.270 - 4.200 mIU/L) on levothyroxine 125 mcg daily. She was later diagnosed with TED. Discussion(s): GD is an autoimmune thyroid disorder related to the presence of TSH receptor-stimulating antibodies and is often associated with ocular symptoms. Activation of an autoimmune response during COVID-19 infection, may induce onset or relapse of GD. A study using the national health insurance service database in South Korea noted an increase in the incidence of subacute thyroiditis in 2020 in association with the COVID-19 pandemic. TED is usually seen in patients with GD. Radioactive iodine is widely used in the treatment of GD and has been associated with development or worsening of TED. There are published cases of TED occurring in patients with GD after receiving COVID-19 vaccine. It is thought that the inflammatory syndrome induced by the adjuvants could induce molecular mimicry, which could trigger TED. In most cases this adverse effect was transient, lasting a few months after treatment. There have been case reports of TED occurring after 3 to 21 days of COVID-19 vaccination in patients with controlled GD. Symptoms improved in 4-8 months. Development of TED in patients with GD who have been treated with RAI typically occurs soon after RAI therapy. For TED to occur in a GD patient 11 months after receiving RAI therapy is unusual. COVID-19 infection appears to have been the trigger for this patient's eye disease. This is highly unusual and has not been published to our knowledge.Copyright © 2023

Using molecular tools to compare various concentration protocols prior to detecting SARS-CoV-2 RNA in wastewater. (Special Issue: Chemistry and global challenges (part A).)

Background: Reports showed presence of SARS-CoV-2 genetic material in wastewater. Wastewater concentration methods are optimized for detection of non-enveloped viruses so need to be adopted for enveloped viruses and their genetic material. Methods: Conventional (cRT-PCR) and quantitative real time RT-PCR (qRT-PCR) were used as readouts to compare 4 water concentration methods namely, (A) filtration on negatively charged membrane followed by extracting RNA from it, (B) adsorbtion-elution method, (C) flocculation with skimmed milk and (D) polyethylene glycol precipitation, to detect SARS-CoV-2 RNA and 229E human coronavirus (229E-HCoV) as a model for spike-containing enveloped virus from fresh and wastewater. Results: On using cRT-PCR: recovery rate of SARS-CoV-2 RNA was better using method A then B for fresh water and method B then D for wastewater. 229E-HCoV recovery from fresh water was better using method C then A and methods B then D for wastewater. On using qRT-PCR, both methods A and B were better for SARS-CoV-2 RNA recovery from both fresh and wastewater. For the 229E-HCoV methods A was the most efficient for fresh water and method B for wastewater. Conclusion: Method B is recommended for SARS-CoV-2 RNA or whole 229E-HCoV recovery from wastewater.

Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

Collided with COVID-19 Pandemic, the 2020 Yangtze Flood Is Exceptionally Severe

During June to July, 2020, persistent heavy precipitation in the Yangtze River Basin (YRB) is resulting in extensive flooding, with over 158 fatalities and tremendous economic losses. This year's disastrous flooding extreme is exceptionally different from those of other years. It contains over 1000-year return period events (for 30-day cumulative precipitation) as observed in Anhui, Guizhou and Sichuan Provinces. The mean precipitation is 308 mm in July 2020, being 54 mm higher than that of July 1998, when serious floods affected the entire Basin causing tremendous socio-economic consequences. Compared with 1998, the short-term (e.g., 1 day) precipitation in YRB did not show significant increases, while the long-term (e.g., 30 days) cumulative precipitation increases significantly. The highest observed 30-day cumulative precipitation is 1221 mm (in Anhui Province) in 2020, while the highest one in 1998 was 1028 mm (in Jiangxi Province). We thus find that this persistent heavy precipitation is the main cause of flooding in 2020. At the same time, TGR may mitigate up 43% of upstream flood, although the main contributors to this year's YRB flood are in the middle and lower reaches. Affected by COVID-19, the number of people at risk in the threatened area are increased, and their capacities to mitigate the dual impacts of COVID-19 pandemic and flooding are hindered since (a) the flooding-caused mitigations may limit people's ability to prevent from virus spreading, and (b) the pandemic is retaining a large amount of migrant workers being within YRB and subject to flooding impacts. Overall, our main discovery is that, although the short-term precipitation in YRB did not increase significantly in 2020, the cumulative one increased significantly in 2020!. © 2022 ISEIS All rights reserved.

Monitoring of the Surfactants in Surface Waters in Slovakia and the Possible Impact of COVID-19 Pandemic on Their Presence

In order to keep the home and occupational environment clean and non-infectious, the consumption of cleaners and disinfectants, including cosmetics, is increasing. Excessive use of these products results in their accumulation in the aquatic environment. Conventional wastewater treatment plants are unable to effectively remove the emergent pollutants, including personal care products. This article is focused on the monitoring of the presence of personal care products in surface waters in two river basins in the Slovak Republic, in terms of the surfactant content. Ecotoxicological evaluation of the selected samples from the monitored river basins was performed by an acute toxicity test using the test organism Daphnia magna. The monitoring results indicate the presence of personal care products in the aquatic environment which poses an ecological and environmental risk. Monitoring in the Hron and Nitra river basins confirmed contamination with the surfactants, to which the measures related to the COVID-19 pandemic contributed. The content of the surfactants in personal care products is significant, and their impact on the aquatic environment is not sufficiently monitored.

Changes of atmospheric metal(loid) deposition from 2017 to 2021 at Mount Emei under China's air pollution control strategy

Acid deposition and particulate matter (PM) pollution have declined considerably in China. Although metal(loid) and acid deposition and PM have many common sources, the changes of metal(loid) deposition in China in the recent decade have not been well explored by using long-term monitoring. Therefore, we analyzed the dry and wet deposition of eleven metal(loid)s (including Al, As, Ba, Cd, Cu, Cr, Fe, Mn, Pb, Sr, and Zn) from 2017 to 2021 at Mount Emei, which is adjacent to the most economic-developed region in western China (Sichuan Basin (SCB)). Anthropogenic emissions contributed to over 80% of the annual wet deposition fluxes of metal(loid)s and acids (SO4 2-, NO3 -, and NH4 +) at Mount Emei, and the major source regions were the SCB, the Yunnan-Guizhou Plateau, and Gansu Province. Metal(loid) and acid deposition had similar seasonal variations with higher wet deposition fluxes in summer but higher wet deposition concentrations and dry fluxes in winter. The seasonal variations were partially associated with higher precipitation but lower pH in summer (968 mm and 5.52, respectively) than in winter (47 mm and 4.73, respectively). From 2017 to 2021, metal(loid) deposition did not decline as substantially as acid deposition (5.6%-30.4%). Both the annual total deposition fluxes and concentrations of Cr, Cu, Sr, Ba, and Pb were even higher in 2020-2021 than in 2017-2018. The inter-annual and seasonal changes implied the responses of metal(loid) deposition to anthropogenic emission changes were buffered (e.g., transformation, dilution, and degradation) by precipitation rates, acidity, natural emissions, and chemical reactions in the atmosphere, among others.Copyright © 2023 Elsevier Ltd

Modeling the Climatic Suitability of COVID-19 Cases in Brazil.

Studies have shown that climate may affect the distribution of coronavirus disease (COVID-19) and its incidence and fatality rates. Here, we applied an ensemble niche modeling approach to project the climatic suitability of COVID-19 cases in Brazil. We estimated the cumulative incidence, mortality rate, and fatality rate of COVID-19 between 2020 and 2021. Seven statistical algorithms (MAXENT, MARS, RF, FDA, CTA, GAM, and GLM) were selected to model the climate suitability for COVID-19 cases from diverse climate data, including temperature, precipitation, and humidity. The annual temperature range and precipitation seasonality showed a relatively high contribution to the models, partially explaining the distribution of COVID-19 cases in Brazil based on the climatic suitability of the territory. We observed a high probability of climatic suitability for high incidence in the North and South regions and a high probability of mortality and fatality rates in the Midwest and Southeast regions. Despite the social, viral, and human aspects regulating COVID-19 cases and death distribution, we suggest that climate may play an important role as a co-factor in the spread of cases. In Brazil, there are regions with a high probability that climatic suitability will contribute to the high incidence and fatality rates of COVID-19 in 2020 and 2021.

Evaluation of SARS-CoV-2 RNA Presence in Treated and Untreated Hospital Sewage.

Wastewater-based epidemiology (WBE) is a potential approach for determining the viral prevalence in a community. In the wake of the COVID-19 pandemic, researchers have begun to pay close attention to the presence of SARS-COV-2 RNA in various wastewaters. The potential for detecting SARS-CoV-2 RNA in hospital sewage could make it an invaluable resource for epidemiological studies. In this regard, two specialized hospitals dedicated to COVID-19 patients were chosen for this investigation. Both hospitals utilize the same wastewater treatment systems. The influent and effluents of the two hospitals were sampled in May and June of 2021, and the samples were evaluated for their chemical properties. According to the findings of this study, the wastewater qualities of the two studied hospitals were within the standard ranges. The sewage samples were concentrated using ultrafiltration and PEG precipitation techniques. The E and S genes were studied with RT-qPCR commercial kits. We found E gene of SARS-CoV-2 in 83.3% (5/6) and 66.6% (4/6) of wastewater samples from hospital 1 and hospital 2, respectively, using ultrafiltration concentration method. Wastewater samples taken after chlorine treatment accounted for 16.6% of all positive results. In addition, due to the small sample size, there was no significant correlation (p > 0.05) between the presence of SARS-CoV-2 in wastewater and the number of COVID-19 cases. Hospitals may be a source of SARS-CoV-2 pollution, thus it is important to monitor and enhance wastewater treatment systems to prevent the spread of the virus and safeguard the surrounding environment.

Multiple Monitoring Stations in Big Cities: First Example of Three Spore Traps in Rome

(1) Background: Rome is a municipality with an area of 1287 km2 and presents floristic-vegetational complexity that is reflected in the composition of aerospora, which are responsible for pollinosis. The presence of airborne pollen can be detected by pollen monitoring. The large extent of the city's territory makes it possible to verify possible changes in pollen composition in different sites of the city. With this in mind, a study was conducted to assess the differences in airborne pollen concentration, considering phenological and production indicators at three different sites in the city. (2) Methods: Pollen data of eight taxa were considered, Alnus spp., Castanea sativa Miller, Cupressaceae-Taxaceae, Olea europaea L., Platanaceae, Poaceae, Quercus spp., and Urticaceae, during 2020 and 2021, using three monitoring samplers. The airborne pollen concentration and the seasons of the three centers were calculated and compared with each other. (3) Results: The diversity between the three samplers shows a phenological succession in accordance with the microclimatic diversity present in the city. The heterogeneity of the airborne pollen concentration reflects the floristic-vegetational diversity, while qualitative and quantitative parameters indicate a homogeneous flowering trend reflecting the seasonality of the various species. (4) Conclusions: The present work and the Italian geographic context suggest the need for a greater number of sampling points to guarantee a true localization of the data. Having several sampling stations also contributes to the protection of health and green areas, which are difficult to manage, conserve, and maintain.

A Retrospective Analysis of National-Scale Agricultural Development in Saudi Arabia from 1990 to 2021

Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime example is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer's accuracies ranging from 83.7% to 94.8% and user's accuracies ranging from 90.2% to 97.9%. The coefficient of determination ((Formula presented.)) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km (Formula presented.) in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km (Formula presented.). While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions. © 2023 by the authors.

Human welfare & emergencies: Education, natality, and violence

I identify changes in human action during emergencies such as heavy precipitation and shelter-in-place orders. In two chapters I explore the effects of precipitation on educational attainment and birth rates as residential broadband access increases. In my third chapter I identify the movement of pets in and out of the home as a trigger for domestic violence during the COVID-19 lockdown. I estimate difference-in-difference regressions with panel data, coming to three conclusions. First: educational attainment in Appalachia is stunted by precipitation, as students have difficulty getting to school in bad weather. As this historically under-educated region of the US gains internet access, the negative effects of rain and snow disappear. Second: there is little evidence that precipitation increases natality. What little evidence of this "blizzard baby" phenomenon I do find is negated by mobile internet access, which decreases births nine months after high precipitation. Third: during COVID-19 shelter-in-place orders, domestic violence decreases after pets are surrendered to local shelters and increases when pets are confiscated. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors

Evaluation of potential capabilities of energy savings and utilization of solar energy in mosques in Hail city, Kingdom of Saudi Arabia

Energy consumption is one of the most important variables that have an impact on the environment. One of the nations in the world with the highest per capita electrical energy usage is the Kingdom of Saudi Arabia. Many attempts are being made in Kingdom of Saudi Arabia to lower energy consumption and electricity consumption to achieve sustainability. In this work, the data on the energy consumption of two mosques in Hail City were analyzed, and the opportunities for energy conservation and the use of solar energy were studied to make mosques sustainable. Annual energy use intensity was determined to be 100 and 121 kWh/m2 for the Al-Khashil and Al-Jamil mosques, respectively. While Al-Khashil's mosque envelope is insulated, energy efficiency measures implemented to the walls of Al-Jamil's mosque resulted in reductions in energy consumption of 27%, 13%, and 6%, respectively. The most effective energy efficiency option is a heating, ventilation, and air conditioning system with a high energy efficiency ratio, which can reduce cooling demand by more than 30%. If the condition of Saudi Building Code 601 is met, then it has the potential to cut energy usage by 35.4% and 63.3% for Al-Khashil and Al-Jamil, respectively. Due to coronavirus disease 2019, Al-Khashil's electricity usage was reduced by 58,737 kWh, or 39.9%, in 2020 compared to 2019. When using data from RETScreen and ATLAS, there were inconsistencies of up to 28%, but for DesignBuilder, the findings were the closest to the billing data. The mosques Al-Khashil and Al-Jamil have a combined yearly photovoltaic energy output from the suggested systems of around 135.93 MWh and 33.98 MWh, respectively. For the mosques, Al-Khashil and Al-Jamil, the yearly yield factor and capacity factor were both 1887.9 kWh/kWp/year and 21.9%, respectively. The annual greenhouse gas emission reductions from photovoltaic systems for Al-Khashil and Al-Jamil were 102.9 tCO2 and 25.72 tCO2, respectively. Concerning economics, the following results were obtained: The levelized cost of energy of photovoltaic systems is 0.0901 SR/kWh (0.024 $/kWh);the net present value and internal rate of return for photovoltaic systems are not suitable as a result of the current prices and the system applied in the Kingdom of Saudi Arabia. If the electricity produced from photovoltaic systems is injected into the grid at a rate of 0.32 SR/kWh, which is comparable to the SEC tariff for the mosque or government sector, then the simple payback time is 5.14 years. © The Author(s) 2023.

Impact of coronavirus-driven reduction in aerosols on precipitation in the western United States.

Among the many impacts of COVID-19, the pandemic led to improved air quality conditions in the countries under quarantine due to the shutdown of industries, drastically reduced traffic, and lockdowns. Meanwhile, the western United States, particularly the coastal areas from Washington to California, received much less precipitation than normal during early 2020. Is it possible that this reduction in precipitation was driven by the reduced aerosols due to the coronavirus? Here we show that the reduction in aerosols resulted in higher temperatures (up to ∼0.5 °C) and generally lower snow amounts but cannot explain the observed low precipitation amounts over this region. In addition to an assessment of the effects of the coronavirus-related reduction in aerosols on precipitation across the western United States, our findings also provide basic information on the potential impacts different mitigation efforts aimed at reducing anthropogenic aerosols would have on the regional climate.

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus.

Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts were analyzed via digital PCR for influenza A, influenza B, norovirus GI, and norovirus GII. Resulting viral loads were normalized on the basis of NH4-N. Our results show a good applicability of ammonia-normalization to compare different wastewater treatment plants. Extracts originally prepared for SARS-CoV-2 surveillance contained sufficient genomic material to monitor influenza A, norovirus GI, and GII. Viral loads of influenza A and norovirus GII in wastewater correlated with numbers from infected inpatients. Further, SARS-CoV-2 related non-pharmaceutical interventions affected subsequent changes in viral loads of both pathogens. In conclusion, the expansion of existing WBE surveillance programs to include additional pathogens besides SARS-CoV-2 offers a valuable and cost-efficient possibility to gain public health information.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis.

BACKGROUND: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.