Public Environmental Management Research Articles

Impact of Political Ideology on Spending Audiences of Brazilian States

Objective: The present study seeks to verify the extent to which affiliation with parties in different locations on the ideological spectrum explains public spending in Brazilian states (2012-2017). Theoretical framework: To achieve the proposed objective, the article in question has a section where the theoretical framework is exposed, consisting of the discussion regarding the classic distinctions between left and right and the relationship with the provision of public policies. Method: Data were collected using the National Treasury Secretariat (STN) databases. Some expenditure functions were chosen which, in principle, would denote ideological divergences between left and right parties and consequently differentiated public spending, namely: public security, social assistance, health, education, urban planning and environmental management. With the database ready, the Chi-square test was carried out to check whether there was an association between ideologies and spending. The Mann-Whitney U test was subsequently applied to compare the median expenditures of the two terms (2012-2014 and 2015-2017). Finally, panel data were used. Results and Discussion: It can be seen that there is no consistent relationship between ideology (E/D) and public spending, that is, left-wing and right-wing parties tend to spend similarly. The exceptions are spending on education and urbanization, which are higher in states led by managers affiliated with left-wing parties, as expected. Implications of the Research: If ideology is a good predictor of political positioning in the legislature, the same cannot be said in the executive and subnational governments. The ideological component here remains in the background and reveals little explanatory power. Originality/Value: Few studies delve into the impact of political ideology on the provision of public policies.

ENVIRONMENTAL IMPACTS OF COVID-19 HOSPITALS AND ARCHITECTURAL REMEDIES

Pandemics, with their potential for widespread devastation and global impact, have historically been a critical concern for public health and society. the historical impact of pandemics on the environment underscores the interconnectedness of human lives and health, societal systems, and the environment, highlighting the need for holistic approaches to public health and environmental management. The COVID-19 pandemic has brought about significant changes in healthcare facilities worldwide, particularly in hospitals designated for COVID-19 patients to counter and serve the impact on humans which universally led to the rapid establishment of specialized hospitals to manage the surge in patients. However, such hospitals not only posed significant challenges to healthcare systems worldwide but also highlighted the need for sustainable practices in designing and operating healthcare facilities along with their significant environmental impacts. In response to these challenges, architects, designers, and healthcare professionals have explored innovative architectural and other remedies to mitigate the environmental impacts of COVID-19 hospitals. This paper explores these potential environmental impacts and proposes architectural planning strategies that include sustainable practices and green design principles through which, by implementing, COVID-19 hospitals or similar hospitals can minimize their environmental footprint and contribute not only to a more sustainable healthcare system but also create healthier and more sustainable environments for patients, healthcare workers, and the community at large. KEYWORDS: Hospital Environmental Impact, Hospital Environment Relation, COVID-19 Hospital Impact on Environment, COVID-19 Hospital Impact, Environmental Impact of Pandemics, Pandemic Hospital Impact on Environment

Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru

<abstract><p>The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O$ _{3} $ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O$ _{3} $ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner.</p></abstract>

Visually Estimating and Forecasting PM2.5 Levels Using Hybrid Architecture Deep Neural Network

Air pollutants, particularly particulate matter with a diameter of less than 2.5 microns (PM2.5), have been linked to a number of negative climatic and health effects, including irritation and damage to internal organs in the short and long term. Knowing current and future levels of PM2.5 is therefore crucial for both public health and environmental management. Photos of the sky and buildings can aid in assessing visibility and air quality because PM2.5 alters the appearance of the sky and its surroundings. This research proposes a method that employs digital images to not only estimate but also forecast PM2.5 concentration levels using deep learning technologies. A convolutional neural network (CNN) was used as the base layer to automatically extract image features, while a long short-term memory (LSTM) network was used as the output layer to analyze the sequence of features. The sky and its surroundings were captured hourly from a high-rise building in Bangkok, Thailand, and the ground-truth PM2.5 data were obtained from the nearest monitoring station facing the same direction. The experimental results show that the hybrid CNN and LSTM model outperformed conventional CNN models in estimating and forecasting PM2.5 levels by 5% and 30%, respectively.

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Abstract. Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3.

A landscape ecology approach: Modeling forest fragmentation with artificial neural networks and cellular‐automata Markov‐chain for improved environmental policy in the southwestern Brazilian Amazon

AbstractLand degradation and forest fragmentation have been prominent issues in the Amazon since the 1970s, primarily driven by the suppression of primary forests due to land use changes. In this study, we propose an innovative approach by integrating artificial neural networks (ANN) and cellular automata Markov chain (CA‐MA) models to predict future land use and assess the associated forest fragmentation using landscape ecology metrics in the Jamari River Basin. The analysis reveals a significant increase in fragmentation between 1985 and 2018, as evidenced by a rise in the number of fragments from 7162 to 28,170. Moreover, we observed a decrease in the core area from approximately 2.5 million hectares to less than 1 million hectares, accompanied by an increase in edge density from 6 to 18 m.ha−1. Additionally, the average distance between fragments expanded from 66 to 95 m. Highlighting the urgency of addressing this issue, our study emphasizes the necessity of exploring effective strategies within the Brazilian public environmental management system. By utilizing various rural policy planning tools, we aim to tackle unsustainable development and mitigate the adverse impacts of land use changes in the region. In conclusion, this research offers an innovative approach that combines ANN and CA‐MA models to predict land use and assess forest fragmentation, shedding light on the alarming trend of land degradation and providing valuable insights for informed decision‐making and sustainable land management practices.

ENVIRONMENTAL COMPONENT OF SUSTAINABLE DEVELOPMENT OF TERRITORIAL COMMUNITIES

The article discusses the development of a target system for stimulating public governance in the context of sustainable development. It emphasizes that current mechanisms of environmental governance are imperfect and do not fully utilize the latest management technologies. The need for the development of measures to enhance the activity of government bodies and the public in the process of making environmental decisions is noted. The sustainable development of society is discussed, highlighting the importance of communication links that influence environmental development. The role of local self-government bodies and state administration in ensuring continuity is emphasized. An emphasis is made on strategic partnership. The impact of human activity on the environment is explored with a focus on environmental risks. The state of Ukraine's ecological system is analyzed before and after the start of full-scale military operations. It is noted that local communities are often better equipped for effective management of the environmental sphere compared to the public and private sectors. Positive practices promoting the sustainable development of territorial communities are reflected. The importance of environmental awareness of the population is recognized, and problems related to low awareness, motivation, and environmental education are identified. The formation of goal-oriented Information-Active Units (IAUs) is proposed to stimulate eco-activities at the level of territorial communities. The proposed system is intended to perform functions such as provision, coordination, and forecasting. The structure of Information-Active Units in the environmental sphere at the level of territorial communities and key elements are presented. A model for coordinating cooperation in the public environmental management system is proposed. The implementation of the proposed measures involves assessing collaboration needs, defining goals, and improving coordination methods. Overall, the importance of collaboration, coordination, and effective communication in the public governance system in the environmental sphere is emphasized to achieve sustainable development goals at the local level.

Preface

Preface

Applying an ecosystem services framework on nature and mental health to recreational blue space visits across 18 countries

The effects of ‘nature’ on mental health and subjective well-being have yet to be consistently integrated into ecosystem service models and frameworks. To address this gap, we used data on subjective mental well-being from an 18-country survey to test a conceptual model integrating mental health with ecosystem services, initially proposed by Bratman et al. We analysed a range of individual and contextual factors in the context of 14,998 recreational visits to blue spaces, outdoor environments which prominently feature water. Consistent with the conceptual model, subjective mental well-being outcomes were dependent upon on a complex interplay of environmental type and quality, visit characteristics, and individual factors. These results have implications for public health and environmental management, as they may help identify the bluespace locations, environmental features, and key activities, that are most likely to impact well-being, but also potentially affect recreational demand on fragile aquatic ecosystems.

A CRISPR-Cas12a powered electrochemical sensor based on gold nanoparticles and MXene composite for enhanced nucleic acid detection

Rapid and accurate detection of nucleic acids plays a critical role in public health, food safety and environmental management. Clustered regularly interspaced short palindromic repeats powered electrochemical sensor (E-CRISPR) is attractive as a point-of-care (POC) testing platform to fulfil this purpose. Yet, bulk noble metals (e.g., gold and platinum) based electrodes that have been widely adopted in E-CRISPR suffer from limited analytical performance and high manufacturing costs. Here, to address this limitation, we present a carbon-based E-CRISPR modified with gold nanoparticles and MXene Ti3C2 (a class of two-dimensional transition metal carbide nanomaterials) that provides highly stable and sensitive transduction of CRISPR-Cas12a trans-cleavage activity. Through systematic evaluation and optimization, our AuNPs/MXene Ti3C2 based E-CRISPR achieve the quantification of human papillomavirus 18 (HPV-18) DNA with a wide range of concentrations from 10 pM to 500 nM with a detection limit of 1.95 pM. We further evaluate the selectivity, degradation resistance and detection capability of the developed sensor during long-term storage. Notably, AuNPs/MXene based E-CRISPR retain more than 70% of initial current after 2 months and deliver reliable analytical results that are unaffected over 42-day storage. Owing to its excellent biofouling-resistant and analytical performance and robust shelf life, our E-CRISPR sensor offers a universal, scalable and low-cost strategy for POC nucleic acid testing.

Composição do fitoplâncton da Baía do Araçá e Canal de São Sebastião, São Paulo, Brasil

Abstract Despite its small area, Araçá Bay (AB) holds cultural, historical, and economic value and displays great benthic biodiversity. Thus, it is crucial to monitor its environmental health, including knowing the main groups of phytoplankton and their temporal variability. The shallow waters of Araçá Bay are continuously modified by the complex hydrography of the adjacent São Sebastião channel (SSC), challenging standard experimental designs for phytoplankton collection. Here we report changes in phytoplankton composition at intervals of five to six weeks from September 2013 to August 2014 in both Araçá Bay and SSC. Samples were collected twice daily for three consecutive days to increase taxonomic resolution. Our goal was to provide an inventory of species occurrences to aid future public policies and environmental management of the area. Analyses revealed high species richness and 166 different phytoplankton taxa. Diatoms and dinoflagellates were always numerically dominant, but taxa occurrence changed markedly. Diatoms of the genera Pseudo-nitzschia were abundant during spring and summer concurrently to signatures of South Atlantic Central Water in the SSC, while Thalassiosira occurred when waters displayed relatively lower salinity. The inventory demonstrated several potentially harmful species of microalgae and cyanobacteria, strongly suggesting investments in monitoring programs in this area that currently experience an increase in population.

Public policies for environmental licensing: a study on municipalization in the state of São Paulo

This article analyzes the implementation of the policy to decentralize public environmental management, focusing on the municipalization of environmental licensing. The mechanisms adopted by the municipality of Araraquara, SP, for its environmental licensing were methodologically compared to the mechanisms used in the municipality of São Carlos, SP, by the state of São Paulo, through the CETESB, which is the state environmental control agency. A bibliographic survey and document analysis of the public agencies were conducted. The research elucidated that municipalized environmental licensing did not decrease the time required to approve licenses and that no investments were made to update the analysis processes, unlike the state, which went through a process to virtualize its license requests. However, the municipality of Araraquara conducts a more thorough analysis of licensed businesses to mitigate environmental impacts. This municipality also has greater contact between municipal public officials and the community, which demonstrates benefits in decentralization.

A Decision Model for Public Economic Environment Monitoring and Management Decision Using Improved Ant Colony Algorithm.

The quality of economic development is not sufficient, despite the apparent role that public environmental monitoring and management play in the economy's rapid expansion. Therefore, enhancing the standard of economic growth is of the utmost importance. It is vital to establish innovative management techniques to support social and governmental transformation in order to raise the standard of economic development. Economic development affects public administration's fundamental duties and goals. The division of public administration distributes social resources and enhances the market's function. The fundamental problem in public economic management is how to successfully actualize rational resource allocation, advance social justice, and boost social welfare on the basis of resource scarcity and the breakdown of the market mechanism. This study greatly increases the global optimization performance of the fundamental ant colony method and provides the explicit programme and simulation stages for the new algorithm. Finally, simulation tests are conducted using the basic ant colony algorithm and the upgraded ant colony algorithm in relation to the decision-making model of public economic management. According to the simulation findings, the revised algorithm successfully addresses the traditional approach's drawbacks, including its slow convergence speed and propensity to easily enter local minima, and its optimization performance has increased by 30.23%.

Lifetime exposure to PM2.5 air pollution and disability-adjusted life years due to cardiopulmonary disease: A modeling study based on nationwide longitudinal data

BackgroundAs knowledge of the health risks of PM2.5 has grown, the focus of PM2.5-related health impact assessments has evolved from simple risk models to burden-of-disease estimates. We proposed an ecological approach to directly estimate the impacts of lifetime exposure to PM2.5 on expected health losses due to cardiopulmonary diseases for older adult populations in Taiwan. MethodsWe created study cohorts of 3.5 million older people living in 350 townships in Taiwan. We used a weighted regression model, with adjustments of area characteristic variables, to directly estimate the impacts of lifetime mean PM2.5 exposure on health losses among the 350 cohorts. Potential avoidable disability-adjusted life years (DALYs) by assuming that ambient PM2.5 met the air quality standard was estimated. ResultsEach 10 μg/m3 increase in lifetime mean exposure to PM2.5 for an individual corresponded to an increment of 0.25 (95 % confidence interval (CI): 0.18–0.32) DALYs due to cardiopulmonary diseases, after adjusting for a wide range of ecological covariates. We estimated that 611.8 (95 % CI: 440.4–783.2) DALYs per 1000 older adult population could potentially be avoided by achieving air quality standards of WHO in Taiwan. ConclusionsReducing PM2.5 pollution in Taiwan associated with significant health co-benefits, providing important implications for public health and environmental management.

Does environmental information disclosure promote the awakening of public environmental awareness? Insights from Baidu keyword analysis

Public environmental awareness (PEA) is critical to the implementation of environmental policies by governments and product strategies by companies, however, there is less literature discussing its drivers. Environmental information disclosure (EID), as a bottom-up environmental policy, promotes public environmental management. This paper takes the Pollution Information Transparency Index (PITI) as a proxy for the degree of EID and discusses its impact on PEA. Using the PITI of 113 cities and the Baidu index-based PEA for 30 provinces from 2010 to 2019, we find a significant increase in PEA with the widening of EID, and the impacts of EID get larger as the quantile increases. Moreover, the effects of EID on PEA are amplified in areas with heavy pollution, high levels of green innovation, and stringent environmental governance capacities, but are diminished in areas with rampant corruption. The findings of this paper provide recommendations for optimizing EID implementation.

Malacological and Parasitological Surveys on Ethiopian Rift Valley Lakes: Implications for Control and Elimination of Snail-Borne Diseases.

Schistosomiasis is one of the snail-borne diseases responsible for the second-highest burden of diseases among neglected tropical diseases. The use of mass drug administration to the populations most at risk is a backbone of the strategy to prevent and control schistosomiasis transmission. However, it offers no protection against re-infection, and humans are often re-exposed when they return to water bodies where snails release cercariae. Surveys on cercarial infection in snails could provide better insights on human disease risk. Hence, in this study, we investigated cercarial infection in snails and also determined the epidemiology of Schistosoma mansoni among fishermen at Ethiopian Rift Valley lakes. Freshwater snails were collected from the shorelines of Ethiopian Rift Valley lakes for examination of cercarial infection during 2020. Environmental data on water quality variables and physical characteristics of snail habitats were collected. Stool samples were collected from fishermen and the Kato-Katz technique was applied for the quantification of Schistosoma mansoni eggs. A malacological survey indicated that six morphologically distinguishable types of cercariae were found in snails. Infected snails with cercaria were more likely present in habitats with high five-day biological oxygen demand and low dissolved oxygen. The overall prevalence of Schistosoma mansoni infection among the fishermen at Ethiopian Rift Valley lakes was found to be 21.5%. This indicates that fishermen at Ethiopian Rift Valley lakes are one of the groups of people harboring schistosome cercariae which are potentially responsible for the transmission of schistosomiasis to lakeshore communities who have contact with lake water. Therefore, complementary medical treatment, public health interventions, environmental management and snail reduction are needed to control the transmission of schistosomiasis.

Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China.

As the builder and maintainer of the city appearance and environment, the city appearance and environmental service industry provides urban residents with a beautiful, livable, and comfortable living environment and is also responsible for providing city appearance and environmental public health services for the economic and social functions carried by the global cities task. Based on this problem, this study has carried out the work of government agencies' public health environmental management and influencing factors based on the Internet of Things and edge computing. This study analyzes the influencing factors of public health environmental management of government agencies based on traditional research and conducts data analysis through the Naive Bayes algorithm. On this basis, the study combines the Internet of Things technology to construct system architecture for public health environmental management. The proposed management system is used to manage government agencies, which meets security, privacy and efficiency, and data security requirements. Experimental results prove that after algorithm optimization and architecture design, the evaluation level of the public health environment is significantly improved.

Prediction of PM2.5 Concentration Based on Time Series

PM2.5 is an important component of air pollution, its chemical composition and source are relatively complex, and it stays in the atmosphere for a long time, which has seriously endangered public health, damaged the ecosystem and even affected the climate and environment. Therefore, the prediction of PM2.5 is of great significance for public health protection and environmental management. One of the reasons why it is difficult to predict PM2.5 is the interaction between air pollutants, so a method that can not only accurately predict the change of PM2.5 concentration, but also explain the results is needed. In this paper, different surface meteorological information is used as time series data to predict the hourly PM2.5 concentration in Wuhan. The prediction can explain the influence of input variables, and use random forest to learn and predict the data. In addition, the meteorological information, air pollutant information and dust value of Zhengzhou City grouped by time series are used as input variables to reflect the air characteristics of North China. This method can learn all the specific time information of the input variables processed by time series in a balanced way, and can also explain the related effects of the input variables. In addition, it also shows that the related variables in North China have an important impact on the generation of PM2.5 in Wuhan.

Ecological insights into the elevational biogeography of antibiotic resistance genes in a pristine river: Metagenomic analysis along the Yarlung Tsangpo River on the Tibetan Plateau

Presently, the prevalence of antibiotic resistance genes (ARGs) is regarded as an emerging environmental issue, and many studies have illuminated biogeographical patterns of the antibiotic resistome. However, few studies have investigated elevational biogeography and associated assembly mechanisms of ARGs in natural river systems. Accordingly, in the present study, we used metagenomics approaches to analyze the biogeographical pattern of ARGs along the pristine Yarlung Tsangpo River on the Tibetan Plateau. Our study retrieved the baseline profiles of ARGs in the pristine river and showed that the ARGs were dominated by bacA, which was resistant to bacitracin and represented more than 91% of total ARGs. The diversity and abundance of ARGs in the pristine river were lower than those in the human-impacted area, suggesting that the antibiotic resistome evolved and was promoted in a human-impacted environment. Furthermore, an elevational distance-decay relationship of ARGs was observed along the pristine Yarlung Tsangpo River, which provided strong evidence of ARG dissimilarity under the elevational variation. Elevational gradients could lead to changes in environmental variables and spatial factors, and consequently alter ARG composition. Elevational gradients could influence the assembly processes of ARGs. The deterministic and stochastic assembly processes both played critical roles and equally participated in shaping ARG composition at the watershed scale along the Yarlung Tsangpo River, and with increasing elevational variation along the river, the ecological processes of ARG assembly shifted from deterministic to stochastic. Moreover, abundant and rare ARGs were tentatively separated to investigate the difference and similarity in their distributions. Although abundant and rare ARGs presented similar distance-decay relationships, rare ARGs were more diverse and vulnerable to the dispersal process and mutation. Overall, our study provides valuable ecological insights to profile the large-scale elevational patterns of ARGs in a pristine river system, thereby providing important information for public health and environmental management.

Emission inventory point source visualization on Google Earth and integrated with HYSPLIT model

Emissions inventories are fundamental tools in the management and research of air pollution, climate change, and other relevant areas of knowledge. This work shows how the Mexico national emissions inventory for criteria pollutants was transferred from an Excel file to a structured and standardized one based on Extensible Markup Language (XML), following the Keyhole Markup Language (KML) standard and the United States Environmental Protection Agency consolidated emissions reporting schema using a Python script (provided as supplementary material). We also show how once in the KML format, the results are compatible with Google Earth and any Geographic Information System (GIS) platforms. The KML format may also allow emissions inventory models to interoperate with Chemical Transport Models (CTM) that would be able to read/write XML files for research and public environmental management policy. As an example, we used Google Earth to engage the point source data and the dispersion of a hypothetical release for that point source modeled using the Hybrid Single Particle Lagrangian Integrated Trajectory developed by the National Oceanic and Atmospheric Administration (HYSPLIT-NOAA), whose outputs also can be displayed on Google Earth. Finally, the KML files outputs from the inventory and HYSPLIT-NOAA model can be visualized on any computer platform and mobile applications that incorporate Google Earth.