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Main effect models contend that perceived social support benefits mental health in the presence and the absence of stressful events, whereas stress-buffering models contend that perceived social support benefits mental health especially when individuals are facing stressful events. We tested these models of how perceived social support impacts mental health during the COVID-19 pandemic and evaluated whether characteristics of everyday social interactions statistically mediated this association – namely, (a) received support, the visible and deliberate assistance provided by others, and (b) pleasantness, the extent to which an interaction is positive, flows easily, and leads individuals to feel understood and validated. 591 United States adults completed a 3-week ecological momentary assessment protocol sampling characteristics of their everyday social interactions that was used to evaluate between-person average values and within-person daily fluctuations in everyday social interaction characteristics. Global measures of perceived social support and pandemic-related stressors were assessed at baseline. Psychiatric symptoms of depression and anxiety were assessed at baseline, at the end of each day of ecological momentary assessment, and at 3-week follow-up. Consistent with a main effect model, higher baseline perceived social support predicted decreases in psychiatric symptoms at 3-week follow-up (β = −.09, p =.001). Contrary to a stress-buffering model, we did not find an interaction of pandemic-stressors × perceived social support. The main effect of perceived social support on mental health was mediated by the pleasantness of everyday social interactions, but not by received support in everyday social interactions. We found evidence for both main effects and stress-buffering effects of within-person fluctuations in interaction pleasantness on daily changes in mental health. Results suggest the importance of everyday social interaction characteristics, especially their pleasantness, in linking perceived social support and mental health.
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Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14-20 days earlier than infected individuals were officially reported. It also showed that community/food markets were "hot spots" for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.
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Various shipping emissions controls have recently been implemented at both local and national scales. However, it is difficult to track the effect of these on PM2.5 levels, owing to the non-linear relationship that exists between changes in precursor emissions and PM components. Positive Matrix Factorisation (PMF) identifies that a switch to cleaner fuels since January 2020 results in considerable reductions in shipping-source-related PM2.5, especially sulphate aerosols and metals (V and Ni), not only at a port site but also at an urban background site. CMAQ sensitivity analysis reveals that the reduction of secondary inorganic aerosols (SIA) further extends to inland areas downwind from ports. In addition, mitigation of secondary organic aerosols (SOA) in coastal urban areas can be anticipated either from the results of receptor modelling or from CMAQ simulations. The results in this study show the possibility of obtaining human health benefits in coastal cities through shipping emission controls.
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The additive manufacturing, also known as 3D-printing, allows for a complete control over the entire manufacturing process that can be tuned to any application. In recent years, it has been tested for its role in various areas like environmental contaminant monitoring, providing solutions for the energy generation and storage and healthcare. To encourage sustainable detection platforms for the pollutants, the sensing electrodes have been reported to be 3D printed because of enhanced electrochemical properties coming from the high surface area of the printed materials. In general, the conventional methods of electrode preparation are time consuming, expensive, and mostly not adaptable. 3D printing however, negates all these challenges. Similarly, in the energy generation and storage field, the rapid and lightweight materials used during 3D printing make them viable and suitable alternative. Since 3D printing is a bottom-up method for the fabrication, the amount of raw material consumed can be tuned and the by-products or wastage minimized. Apart from these demanding areas, additive manufacturing, in last 2 years which witnessed epidemic outspread, has supported health sector immensely in fight against COVID. 3D printing allowed the rapid manufacturing of COVID-19 detection kits and helped maintain the COVID-19 safeguards in place. Sample collection swabs, respirators and other components of the PPE kits were among many products developed using 3D printing during the pandemic. Keeping these things in mind, this review encapsulates the use of 3 D printing for energy application, detection of water and biological contaminants and as safeguard tool during covid pandemic.
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Open source Geographic Information System (GIS) have been fostering spatial data research such as Earth observation and environmental monitoring for more than 30 years. More recently, globally available geospatial information combined with web technologies are providing new environments and tools for data handling. Thus, binding the mapping and processing capabilities of traditional GIS to the accessibility and reliability of web-based data providers can bring new opportunities for research. In this paper, we built a QGIS plugin to explore the integration of different public data providers in Brazil along with field data produced by the BONDS project. The biOdiversity conservatioN with Development in Amazon wetlandS project (BONDS) proposes to develop biodiversity scenarios for the Amazonian floodplains aiming to support solutions to preserve biodiversity and ecosystem services. The use of web services enabled dynamic and fast access to several products ranging from remote sensing images, land use and land cover, territorial cartography, water quality, to COVID-19 health data, and more. © 2022 National Institute for Space Research, INPE. All rights reserved.
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Given the actual risk of poliomyelitis outbreaks in the region due to poliovirus derived from the Sabin vaccine or the importation of wild poliovirus, the Latin American Society of Pediatric Infectious Diseases commissioned an ad hoc group of experts from the institution's Vaccines and Biologicals Committee, to draft an official position paper on the urgent need to increase immunization levels against the disease in the region and incorporate inactivated polio vaccine exclusive schedules in all national immunization programs. This publication discusses the main conclusions and recommendations generated as a result of such activity.Copyright © 2022, Sociedad Chilena de Infectologia. All rights reserved.
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A method for determining the presence of SARS-CoV-2 RNA in HEPA filters from portable air cleaners (PACs) have been developed and validated. Herein, a monitoring survey was conducted for 13 weeks in three indoor environments, school, nursery and a household of a socio-sanitary center in Ciudad Real, Spain. In this study, we employed environmental monitoring by RT-PCR of the presence of SARS-CoV-2 in HEPA filters and other surfaces of these indoor spaces for a selective screening in asymptomatic population groups. The aim was to limit outbreaks in an early stage. Only one HEPA filter tested positive in the socio-sanitary center. After analysis by RT-PCR of SARS-CoV-2 in residents and healthcare workers, one worker tested positive. Therefore, this study provides direct evidence of virus-containing aerosols trapped in HEPA filters and the possibility of using these PACs for environmental monitoring of SARS-CoV-2 while they remove airborne aerosols and trap the virus. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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An efficient and punctual monitoring of air pollutants is very useful to evaluate and prevent possible threats to human beings' health. Especially in areas where such pollutants are highly concentrated, an accurate collection of data could suggest mitigation actions to be implemented. Moreover, a well-performed data collection could also permit the forecast of future scenarios, in relation to the seasonality of the phenomenon. With a particular focus on COVID pandemic period, several literature works demonstrated a decreasing of pollutant concentrations in air of urban areas, mainly for NOx, while CO and PM10, on the opposite, has been observed to remain still, mainly because of the intensive usage of heating systems by the people forced to stay home (on specific regions). With the present contribution the authors here present an application of Time Series analysis (TSA) approach to pollutants concentration data of two Italian cities during first lockdown (9 march – 18 may 2020), demonstrating the possibility to predict pollutants concentration over time. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.
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International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021)The international conference on "Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial sci nce in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.
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Background: Spore Trap is an environmental detection technology, already used in the field of allergology to monitor the presence and composition of potentially inspirable airborne micronic bioparticulate. This device is potentially suitable for environmental monitoring of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in hospital, as well as in other high-risk closed environments. The aim of the present study is to investigate the accuracy of the Spore Trap system in detecting SARS-CoV-2 in indoor bioaerosol of hospital rooms. Methods: The Spore Trap was placed in hospital rooms hosting patients with documented SARS-CoV-2 infection (n = 36) or, as a negative control, in rooms where patients with documented negativity to a Real-Time Polymerase Chain Reaction molecular test for SARS-CoV-2 were admitted (n = 10). The monitoring of the bioaerosol was carried on for 24 h. Collected samples were analyzed by real-time polymerase chain reaction. Results: The estimated sensitivity of the Spore Trap device for detecting SARS-CoV-2 in an indoor environment is 69.4% (95% C.I. 54.3-84.4%), with a specificity of 100%. Conclusion: The Spore Trap technology is effective in detecting airborne SARS-CoV-2 virus with excellent specificity and high sensitivity, when compared to previous reports. The SARS-CoV-2 pandemic scenario has suggested that indoor air quality control will be a priority in future public health management and will certainly need to include an environmental bio-investigation protocol.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/diagnosis , Hospitals , Pandemics , HospitalizationABSTRACT
The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.
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The COVID-19 pandemic has had a dramatic socio-economic impact on mankind;however, the COVID-19 lockdown brought a drastic reduction of anthropic impacts on the environment worldwide, including the marine–coastal system. This study is concentrated on the Mar Piccolo basin of Taranto, a complex marine ecosystem model that is important in terms of ecological, social, and economic activities. Although many numerical studies have been conducted to investigate the features of the water fluxes in the Mar Piccolo basin, this is the first study conducted in order to link meteo-oceanographic conditions, water quality, and potential reduction of anthropic inputs. In particular, we used the model results in order to study the response of the Mar Piccolo basin to a drastic reduction in the leakage of heavy metal IPAs from industrial discharges during the two months of the mandated nationwide lockdown. The results show the different behavior of the two sub-basins of Mar Piccolo, showing the different times necessary for a reduction in the concentrations of heavy metals even after a total stop in the leakage of heavy metal IPAs. The results highlight the high sensitivity of the basin to environmental problems and the different times necessary for the renewal of the water in both sub-basins. © 2023 by the authors.
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Legionella is a pathogen that colonizes soils, freshwater, and building water systems. People who are most affected are those with immunodeficiencies, so it is necessary to monitor its presence in hospitals. The purpose of this study was to evaluate the presence of Legionella in water samples collected from hospitals in the Campania region, Southern Italy. A total of 3365 water samples were collected from January 2018 to December 2022 twice a year in hospital wards from taps and showers, tank bottoms, and air-treatment units. Microbiological analysis was conducted in accordance with the UNI EN ISO 11731:2017, and the correlations between the presence of Legionella and water temperature and residual chlorine were investigated. In total, 708 samples (21.0%) tested positive. The most represented species was L. pneumophila 2-14 (70.9%). The serogroups isolated were 1 (27.7%), 6 (24.5%), 8 (23.3%), 3 (18.9%), 5 (3.1%), and 10 (1.1%). Non-pneumophila Legionella spp. represented 1.4% of the total. Regarding temperature, the majority of Legionella positive samples were found in the temperature range of 26.0-40.9 °C. An influence of residual chlorine on the presence of the bacterium was observed, confirming that chlorine disinfection is effective for controlling contamination. The positivity for serogroups other than serogroup 1 suggested the need to continue environmental monitoring of Legionella and to focus on the clinical diagnosis of other serogroups.
Subject(s)
Legionella pneumophila , Legionella , Humans , Chlorine/analysis , Water Supply , Hospitals , Environmental Monitoring , Water/analysis , Water MicrobiologyABSTRACT
There is a lot of discussion on how viruses (such as influenza and SARS-CoV-2) are transmitted in air, potentially from aerosols and respiratory droplets, and thus it is important to monitor the environment for the presence of an active pathogen. Currently, the presence of viruses is being determined using primarily nucleic acid-based detection methods, such as reverse transcription- polymerase chain reaction (RT-PCR) tests. Antigen tests have also been developed for this purpose. However, most nucleic acid and antigen methods fail to discriminate between a viable and a non-viable virus. Therefore, we present an alternative, innovative, and disruptive approach involving a live-cell sensor microdevice that captures the viruses (and bacteria) from the air, becomes infected by them, and emits signals for an early warning of the presence of pathogens. This perspective outlines the processes and components required for living sensors to monitor the presence of pathogens in built environments and highlights the opportunity to use immune sentinels in the cells of normal human skin to produce monitors for indoor air pollutants.
Subject(s)
Air Pollutants , COVID-19 , Viruses , Humans , SARS-CoV-2 , COVID-19/diagnosis , Respiratory Aerosols and DropletsABSTRACT
In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.
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Microfluidic biosensors integrating fluid control, target recognition, as well as signal transduction and output, have been widely used in the field of disease diagnosis, drug screening, food safety and environmental monitoring in the past two decades. As the central part and technical characteristics of microfluidic biosensors, the fluid control is not only associated with accuracy and convenience of the sensors, but also affects the material selection and working mode of the sensors. This review summarizes the fluid driving forces for microfluidic biosensors, including gravity, capillary force, centrifugal force, pressure, light, sound, electrical, and magnetic forces. Then, the recent advances in microfluidic biosensors for the detection of viruses, cells, nucleic acids, proteins and small molecules are discussed. Finally, we propose the current challenges and future perspectives of microfluidic biosensors. We hope this review can provide readers with a new perspective to understand the technical characteristics and application potential of microfluidic biosensors.Copyright © 2022 Elsevier B.V.
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The purpose of this paper is to enhance the performance of the virtual assistant. So, what exactly is a virtual assistant. Application software, often called virtual assistants, also known as AI assistants or digital assistants, is software that understands natural language voice commands and can perform tasks on your behalf. What does a virtual assistant do. Virtual assistants can complete practically any specific smartphone or PC activity that you can complete on your own, and the list is continually expanding. Virtual assistants typically do an impressive variety of tasks, including scheduling meetings, delivering messages, and monitoring the weather. Previous virtual assistants, like Google Assistant and Cortana, had limits in that they could only perform searches and were not entirely automated. For instance, these engines do not have the ability to forward and rewind the song in order to maintain the control function of the song;they can only have the module to search for songs and play them. Currently, we are working on a project where we are automating Google, YouTube, and many other new things to improve the functionality of this project. Now, in order to simplify the process, we've added a virtual mouse that can only be used for cursor control and clicking. It receives input from the camera, and our index finger acts as the mouse tip, our middle finger as the right click, and so forth.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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Due to the COVID‐19 pandemic, governments imposed several mobility restrictions which can be used to evaluate their impact on air quality and generate better‐targeted policies to improve it. Therefore, this study aimed to define sustainable mitigation measures to reduce air pollution based on quantifying the impacts of the restrictions imposed during the COVID‐19 pandemic on air quality in Portugal. Thus, hourly concentrations of PM10, PM2.5, NO2, O3, CO and SO2 were obtained from the Portuguese Air Quality Monitoring Network. Data was then divided into six periods (2020–2021) and compared with the corresponding historical periods (2015–2019). Furthermore, the satellite data of NO2, CO, and absorbing aerosol index (AAI) from the sentinel‐5P TROPOMI was collected to complement the analysis conducted for the monitoring data. Overall, air quality improved in all study periods and areas, except in industrial sites. The satellite data corroborated the results herein achieved and thus validated the real effect of the measures adopted in the country during the pandemic on air quality. Sustainable policies to improve air quality could include remote (or hybrid) work whenever possible as a long‐term measure and prohibition of travelling between municipalities when an extraordinary event of high air pollution is predicted or occurs. Other policies should be adopted for industrial areas. Given this, and as the restrictive mobility measures had a strong effect on reducing air pollution, the post‐COVID era represents an opportunity for society to rethink future mobility and other emerging policies, that should favour softer and cleaner means of transportation.
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Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions;however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism. © 2022 The Author(s)
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The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay. © 2022 Elsevier B.V.