Environmental Protection Department Research Articles

Road traffic noise assessment method for Hong Kong and associated GIS-based assessment tools

Hong Kong, known for its compact and complex urban environment, has long faced unique challenges in modeling road traffic noise for planning and assessment purposes. The Environmental Protection Department (EPD) of the Hong Kong Special Administrative Region Government (HKSARG) identified and established an enhanced prediction methodology called Road Traffic Noise Assessment Method - Hong Kong (RONOSS-HK). RONOSS-HK aims to provide better treatment on source geometry, source terms, noise propagation, considerations for new-energy vehicles and special noise reduction of the Innovative Noise Mitigation Designs (INMD) to cater for the complicated spatial noise situations in Hong Kong. To facilitate the implementation of RONOSS-HK, the EPD has also developed and introduced a GIS-based assessment tool which allows users to utilize GIS data readily available in HKSARG databases and other spatial information in the public domain for accurate road traffic noise prediction using RONOSS-HK methodology. Additionally, the EPD is in the process of further developing a web-based 3D software to serve as an alternative comprehensive assessment tool for applying RONOSS-HK in noise assessment. This paper introduces RONOSS-HK prediction methodology and the latest development of its associated GIS-based assessment tools.

Development of a new hexaploid spring wheat (Triticum aestivum L.) variety “NIFA NIJAT-23” for commercial cultivation in irrigated areas of Khyber Pakhtunkhwa

Sustainable wheat farming requires an ongoing process of variety development, release, and quality seed production of new high yielding and disease resistant varieties. Farmers in Khyber Pakhtunkhwa are currently cultivating several wheat cultivars, including NIFA AMAN-17, NIFA AWAZ-19, PIRSABAK-21, ZARGHUN-21, PIRSABAK-19, GULZAR-19, KHAISTA-17, PIRSABAK-15, INSAF-15, KP-15 and LALMA-13 under both irrigated and rainfed circumstances. The frequent changes in disease virulence, particularly yellow rust and leaf rust observed frequently over the last three years in KP, have forced breeders to develop new resistant varieties in order to increase productivity. The new hexaploid spring wheat variety NIFA NIJAT-23 (CTHN-162056) developed at NIFA has demonstrated resistance to yellow rust during testing in the Khyber Pakhtunkhwa Wheat Yield Trials (KPWYT) at 13 distinct locations in 2019-20, and this variety with a grain yield of 3337 kg ha-1 has been placed at the third position among 24 test entries. Under normal planting conditions in 2020-21, the NUWYT (National Uniform Wheat Yield Trial) pooled analysis revealed that CTHN-162056 secured the 1st position and provided the greatest mean grain yield of 4701 kg ha-1 across all the candidate lines throughout Pakistan at 31 locations. According to the data reported by the Crop Diseases Research Institute (CDRI), Department of Plant and Environmental Protection, National Agricultural Research Centre (NARC), Islamabad, it has demonstrated highly desirable relative resistance index (RRI) for two consecutive years (2020-21 & 2021-22). The Provincial Seed Council (PSC), Khyber Pakhtunkhwa approved CTHN-162056 and registered as a potential variety “NIFA NIJAT-23”.

Design and Analysis of Small Fallen Leaf Collection, Crushing, and Recycling Vehicle

Leaf recycling, a crucial component of urban environmental protection, aims to systematically collect and reuse fallen leaves on streets. At present, fallen leaves are usually cleaned manually by sanitation workers, and environmental protection departments then use special cars to transport fallen leaves to treatment stations for incineration. This not only requires a significant amount of labor and material resources but also causes air pollution and wastes fallen leaves. In this study, a collecting, crushing, and recycling vehicle was designed to solve this problem. The proposed device significantly improves the efficiency of collecting fallen leaves and facilitates their secondary utilization. The design process is described in detail, including the working design of the entire device, static analysis, and transmission system design. The entire vehicle model was 3D printed to verify that the initial design concept satisfies the assembly and production requirements of the device.

Assessing and anticipating environmental challenges in Lahore, Pakistan: future implications of air pollution on sustainable development and environmental governance.

Urban environment and air quality are changing primarily due to land use land cover (LULC) changes, economic activity, and urbanization. Air pollution has been increasingly acknowledged as a major issue for cities due to its extensive effects on health and well-being. As the second most populous city in the country, Lahorefaces alarming levels of air pollutants, whichinduced this study to focus on the pervasive issue of air pollution in Lahore. For this, the study collected air pollutants data from the Environmental Protection Department of Punjab and analyzed them using the ARIMA model. In the research results, both the observed data and predictive models uncovered concerning trends in pollutant concentrations, ultimatelyportraying a concerning picture for air quality management. Carbon monoxide (CO) levels show a consistent rise, surpassing Pakistan's environmental standards by 2025. Similarly, nitrogen dioxide (NO2) concentrations escalate, exceeding prescribed standards. Ground-level ozone (O3) also demonstrates a substantial increase, surpassing standards by 2025. Both PM2.5 and PM10 exhibit marked upward trends, projected to exceed recommended limits, particularly PM10 throughout the study year. The Air Quality Index exhibits an observable upward trend, fluctuating between 70 and 442 from 2015 to 2020. Similarly, a positive correlation was found between population growth and land use conversion into residential areas. Projections suggest a continuous increase, potentially hitting a severe level of 500 during winter by 2025. These findings point to an impending air pollution crisis, demanding urgent action to address the hazardous situation in the city. The study recommends that urban air pollution should be reduced, and the negative health effects of air pollution should be minimized using vegetation barriers, screens, and greening initiatives. Strict regulations and monitoring initiatives need to be put in place in big cities to monitor pollution and vegetation.

Conflict or coordination? The cross-departmental interaction in local environmental governance of China

Organizational coordination has been regarded as a vital factor to determine the environmental governance efficiency of local authority, while existing empirical studies mainly discuss the vertical decentralization but largely ignore the horizontal cross-departmental interaction within single government. Based on the data of 31 Chinese provinces from 2003 to 2017, this paper uses a panel vector autoregression (PVAR) approach, two-way fixed-effect models and dynamic panel regression with system generalized method of moments estimation (SYS-GMM) to investigate the environmental strategic interaction between two representative departments (finance department and environmental protection department) within local governments. The results indicate, on the whole, their environmental governance strategies show a significantly co-directional coordination. However, the cross-departmental interaction obviously varied in different periods and regions (even showed as conflict sometimes), and was synchronized with the dynamic trade-off between economic and environmental targets of China. This research helps understand the inner logic of environmental governance of Chinese government, and can be valuable guidance for other countries to accomplish multi-departmental sustainable development goal.

A multidirectional pairwise coupling approach with spectral features unmixing to quantify total phosphorus, total nitrogen, and chlorophyll-a in urban rivers

Comprehensive and effective water quality monitoring is vital to water environment management and prevention of water quality from degradation. Unmanned aerial vehicle (UAV) remote sensing techniques have gradually matured and prevailed in monitoring water quality of urban rivers, posing great opportunity for more effective and flexible quantitative estimation of water quality parameter (WQP) than satellite remote sensing techniques. However, current UAV remote sensing methods often entail large quantities of cost-prohibitive in-situ collected training samples with corresponding chemical analysis in different monitoring watersheds, laying time and fiscal pressure on local environmental protection department. They suffer relatively low calculation accuracy and stability and their applicability in various watersheds is constrained. This study developed a unified two-stage method, multidirectional pairwise coupling (MDPC) with information sharing and delivery of different modeling stages to efficiently predict concentrations of WQPs including total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chl-a) from hyperspectral data. MDPC incorporates exterior and interior feature interaction and gravity model variant to improve prediction accuracy and stability with consideration of mutual effect in the proximity. The structure design and workflow of MDPC ensure high robustness and application prospect due to achievement of good performance with less training samples, improving applicability and feasibility. The experiments show that MDPC has achieved good performance on retrieval of WQPs concentrations including TP, TN, and Chl-a, the results mean absolute percent error (MAPE) and coefficient of determination (R2) ranging from 6.34 % to 11.94 % and from 0.74 to 0.93. This study provides a systematic and scientific reference to formulate a feasible and efficient water environment management scheme.

Intelligent classification of maize straw types from UAV remote sensing images using DenseNet201 deep transfer learning algorithm

China has abundant straw resources, but challenges in utilization persist. Utilization rates need improvement, and environmental pollution from straw burning remains a significant issue. Accurate and intelligent remote sensing classification of straw types is crucial for enhancing straw utilization and preventing straw burning. This paper proposed a new approach for the intelligent classification of maize straw types, using the DenseNet201 deep transfer learning algorithm based on RGB images captured by Unmanned Aerial Vehicle (UAV). The sample labels dataset was established for maize straw types, utilizing DenseNet201 deep transfer learning algorithm to pre-train the sample set. This pre-training facilitated model transfer and parameter initialization. Subsequently, the second round of deep transfer learning was performed to construct the final maize straw type remote sensing classification models using DenseNet201 deep transfer learning algorithm. This model and results were subsequently compared with maize straw type classification by the ResNet50 and GoogLeNet deep transfer learning algorithms, as well as maize straw type classification using DenseNet201, ResNet50, and GoogLeNet deep learning algorithms. The results showed that the accuracy of the pre-trained maize straw type deep transfer learning remote sensing classification model surpassed that of the untrained maize straw type deep learning remote sensing classification model, resulting in an enhancement of accuracy by 8.59%, 7.38%, and 1.28%, respectively. The DenseNet201 deep transfer learning model for maize straw types exhibited the highest accuracy with the overall accuracy of 95.57%, and the kappa coefficient of 0.9410. Hence, the DenseNet201 deep transfer learning classification of maize straw types enabled the attainment of intelligent remote sensing recognition of maize straw types. The classification methodology, model, and results presented in this paper can serve as valuable technical references, offering essential information support for agricultural and environmental protection departments actively involved in the comprehensive utilization of straw resources and atmospheric environmental protection efforts.

Assessing Borneo’s tropical forests and plantations: a multi-sensor remote sensing and geospatial MCDA approach to environmental sustainability

The assessment of environmental sustainability is of utmost importance for the forests and plantations in Borneo, given the critical need for environmental protection through the identification and mitigation of potential risks. This study was conducted to assess the environmental sustainability of tropical forest and plantations landscape, a case study in northern Sabah, Malaysian Borneo. Applications of the latest high-resolution multi-sensor remote sensing and geospatial MCDA are cost-effective and useful for large-scale environmental sustainability assessment. The land use land cover (LULC) of the study area was mapped with synergistic use of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical and high-resolution PlanetScope satellite imageries, resulting in overall accuracy of 87.24%. Five sustainability indicator layers: slope erosion protection, river buffer, landscape connectivity and quality, high conservation value (HCV), and water turbidity were developed from the LULC map, ancillary datasets of SRTM, and forest operation basemap with reference to standards from the Environment Protection Department (EPD), Roundtable on Sustainable Palm Oil (RSPO), and Forest Management Plan (FMP) for the analysis using multi-criteria decision analysis (MCDA) model. The results revealed that overall, the study areas are in the high sustainability category at 61%, medium at 31%, and low at only 8%. We analyzed the environmental sustainability of five land use boundaries, and the results showed that Industrial Tree Plantations (ITP) and Village Reserve are mostly in the high category. Meanwhile, oil palm plantations, rubber plantations, and forest reserve (FR) are the majority in the medium category. Both oil palm and rubber plantations are a majority in the medium class due to monocropping land use type having low landscape connectivity and quality individual sustainability indicator layer. The study presented the concept of use of multi-sensor remote sensing for LULC mapping with geospatial MCDA for environmental sustainability assessment useful to stakeholders for improving the management plan also contributing toward the progress of achieving UNSDGs and addressing REDD+.

Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

Cross-sectional study of association between caries and fluoridated water among third-grade students in Pennsylvania

BackgroundCaries is the most common chronic disease among children. In Pennsylvania, a comprehensive oral health Basic Screening Survey and assessment of the association between caries and community water fluoridation (CWF) among children have not been conducted. MethodsFrom 2021 through 2022, the first Basic Screening Survey was conducted among third-grade students in Pennsylvania. Oral health and demographic data were collected. CWF data were provided by the Pennsylvania Department of Environmental Protection. The relative risk of developing caries in association with CWF was assessed using the GENMOD procedure in SAS, Version 9.4 (SAS Institute) in this cross-sectional study. ResultsCaries prevalence was 59.7% among 4,120 screened students. Participation in the free or reduced lunch program and CWF were each significantly associated with risk of developing caries after adjustment for age, sex, and race and ethnicity. The risk of developing caries was 33% higher among students who participated in the free or reduced lunch program than those who did not participate (relative risk, 1.33; 95% CI, 1.24 to 1.42). Students with the highest CWF coverage had a nearly 16% lower risk of developing caries than those without CWF coverage (relative risk, 0.84; 95% CI, 0.75 to 0.94). ConclusionsCWF was significantly associated with a reduced risk of developing caries. Efforts are needed to increase CWF coverage, along with promotion of oral health education and healthy dietary habits among Pennsylvania children. Practical ImplicationsIncreasing CWF coverage will reduce caries burden among Pennsylvania children. Although Pennsylvania has no state laws to regulate CWF, these findings are persuasive for local municipalities to consider expanding CWF coverage.

Coordination of interests between local environmental protection departments and enterprises under China’s environmental regulation policies: an evolutionary game theoretical approach

The Central Inspection for Ecological and Environmental Protection (CIEEP) and Environmental Vertical Management Reform (EVMR) policies have reshaped the relationships among stakeholders and the power structure in local environmental governance systems. To reveal the current local environmental collaborative governance mechanism, this study placed CIEEP and EVMR policies within the same research framework and considered multiple governance entities, including the central government, higher-level environmental protection departments, local environmental protection departments, enterprises, and the public, employing evolutionary game theory and numerical simulation techniques to explore the boundary conditions, key influencing factors, influence mechanisms, and realization paths of local environmental collaborative governance from a dynamic process perspective. The results show that: 1) The cost of enterprise environmental behavior, intensity of reward and punishment for enterprise environmental behavior, government subsidies, supervision cost of local environmental protection departments, and intensity of reward and punishment for local environmental protection departments all have important effects on local environmental governance; 2) Compared with the cost of enterprise environmental behavior, enterprise environmental behavior is more sensitive to changes in supervision cost of local environmental protection departments, and the intensity of reward and punishment for enterprise environmental behavior has the same major impact on enterprise environmental behavior as that of local environmental protection departments; 3) The influence of CIEEP policy on enterprise environmental behavior is unstable, showing signs of strategic response. Therefore, local environmental governance systems should be further optimized by encouraging local environmental protection departments to actively supervise and designing differentiated policy tools. This study provides a reference for solving local environmental governance problems under current environmental regulation policies.

Study on the regulatory alienation effect of China's Environmental Protection Tax Law during the strict epidemic control period: based on a sample test of 1000 tax personnel

The enactment of the Environmental Protection Tax Law was a major event in the process of China's economic and social development. This law has achieved certain results in the first two years since enactment, but there have also been many problems. During the three-year strict pandemic control period, the normal process of Environmental Protection Tax Law implementation was impacted, causing alienation and reducing the quality of implementation. This study empirically reveals the alienation mechanism in the implementation of the Environmental Protection Tax Law by taking 1000 Chinese tax professionals, and randomly divides the sample equally into two parts, utilizing 500 samples for the initial test and another 500 samples for the robustness test. This research shows that pollutant emission monitoring (correlation coefficient is 0.07), preferential tax policies (correlation coefficient is 0.03) and the quality of tax personnel (correlation coefficient is 0.06) are not conducive to realizing the crowding-out effect of the Environmental Protection Tax Law, while preferential tax policies (correlation coefficient is 0.05), information-sharing platforms (correlation coefficient is 0.06) and the quality of tax personnel (correlation coefficient is 0.05) hinder the incentive effect, and the remaining elements passed the correlation coefficient test within certain confidence intervals. Therefore, the implementation of China's Environmental Protection Tax Law should be improved in the following ways: the accuracy of the environmental protection departments' supervision of pollutant emissions as well as the scientificity and rationality of preferential tax policies should be improved, the construction of environmental protection tax information-sharing platforms should be strengthened, and compound tax collection and management talent should be created.

Preparation and Adsorption Studies of Heavy Metal Ions on Magnetic Fe3O4 Hollow Microspheres

Similar to the dilemma faced by developed countries in the past, as China's industrialisation continues to develop, the environmental problems it causes are becoming increasingly serious. In particular, industrial wastewater, which is discharged directly from factories into the surrounding environment, has become the biggest hidden threat to the environment. Therefore, the treatment of industrial wastewater has always been the focus of environmental protection departments and relevant technical personnel. For the human body, heavy metal ions in wastewater are not only highly toxic and easily absorbed, but can also accumulate in organisms through the food chain. Therefore, finding an efficient, inexpensive and simple method to treat heavy metal ions in wastewater is the most urgent issue for scientific research.

Quantifying Disparities in Per- and Polyfluoroalkyl Substances (PFAS) Levels in Drinking Water from Overburdened Communities in New Jersey, 2019-2021.

Policymakers have become increasingly concerned regarding the widespread exposure and toxicity of per- and polyfluoroalkyl substances (PFAS). While concerns exist about unequal distribution of PFAS contamination in drinking water, research is lacking. We assess the scope of PFAS contamination in drinking water in New Jersey (NJ), the first US state to develop regulatory levels for PFAS in drinking water. We test for inequities in PFAS concentrations by community sociodemographic characteristics. We use PFAS testing data for community water systems (CWS) () from the NJ Department of Environmental Protection (NJDEP) from 2019 to 2021 and demographic data at the block group level from the US Census to estimate the demographics of the NJ population served by CWS. We use difference in means tests to determine whether CWSs serving "overburdened communities" (OBCs) have a statistically significant difference in likelihood of PFAS detections. OBCs are defined by the NJDEP to be census block groups in which: a) at least 35% of the households qualify as low-income, b) at least 40% of the residents identify as people of color, or c) at least 40% of the households have limited English proficiency. We calculate statewide summary statistics to approximate the relative proportions of sociodemographic groups that are served by CWSs with PFAS detections. We find that 63% of all CWSs tested by NJDEP from 2019 to 2021 had PFAS detections in public drinking water, collectively serving 84% of NJ's population receiving water from CWSs. Additionally, CWSs serving OBCs had a statistically significant higher likelihood of PFAS detection and a higher likelihood of exposure above state MCLs. We also find that a larger proportion of people of color lived in CWS service areas with PFAS detections compared to the non-Hispanic white population. These findings quantitatively identify disparities in PFAS contamination of drinking water by CWS service area and highlight the extent of PFAS drinking water contamination and the importance of PFAS remediation efforts for protecting environmental health and justice. https://doi.org/10.1289/EHP12787.

Environmental protection taxes and green productivity: Evidence from listed companies in China

Enforcing an environmental protection tax is a pivotal institutional measure for addressing environmental and climate change challenges and marking a substantial advancement in ecological development. This study aims to explore the influence of environmental protection taxes on the "green" productivity of the manufacturing industry through an analysis of Chinese listed companies. Initially, the Dynamic Slacks-Based Measure (DSBM) method is employed to estimate the "green" production efficiency levels of 175 listed companies in China from 2016 to 2020. Subsequently, by aligning pollution tax standards issued by local governments and relevant environmental protection departments with microlevel data from the CSMAR database, wind database, listed company annual reports, and social responsibility reports, the sample was refined to 833 data points. Using the implementation of the environmental protection tax policy in 2018 as a natural experiment, a Difference-in-Differences (DID) model is applied for empirical testing. The empirical results reveal a counterintuitive negative correlation between the environmental protection tax and the "green" productivity of companies, contradicting the predictions of the Porter hypothesis. This suggests that stringent environmental regulations hinder the development of green technologies. The adverse impact of environmental protection taxes on "green" productivity is attributed to heightened environmental legitimacy pressures and increasing environmental uncertainty risks companies face. Further exploratory analysis indicated that the digital economy and green finance have a positive moderating effect, significantly mitigating the negative impact of environmental protection taxes on companies' overall "green" productivity. Consequently, dynamically optimizing environmental tax policies is imperative to alleviate companies' environmental legitimacy pressures and uncertainty risks. Additionally, capitalizing on the opportunities the digital economy presents and leveraging green finance policies have emerged as effective strategies for realizing the Porter effect of environmental protection taxes.

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

Obtaining an important component of humate-enriched organomineral fertiliser based on oxidised lignite from Kyrgyzstan

The study of methods for the production and processing of humateenriched organomineral fertilisers based on oxidised lignite represents a key aspect for modern agriculture, due to the need to improve resource efficiency, soil quality, and sustainability of production. The purpose of this study was to determine the best methods of obtaining and processing humate-enriched organomineral fertilisers based on oxidised lignite. To fulfil this purpose, experimental research was carried out in the laboratory of the Institute of Natural Resources of the Southern Branch of the National Academy of the Kyrgyz Republic and the educational and scientific laboratory of the Department of Ecology and Environmental Protection of the Osh Technological University named after M.M. Adyshev. The findings of the study indicate significant changes in soil characteristics after application of vermicompost and glauconite. Analyses of vermicompost revealed that it has a significant content of nutrient elements and organic compounds. The potassium content of vermicompost increased by 40%, while silicon and phosphorus content increased by 39% and 28.5%, respectively. As a result of vermicomposting, there is an improvement in soil characteristics: pH increases to 7-7.5, organic matter content increases to 35-45%, while C/N-ratio decreases to 10-12, which creates more favourable conditions for plant growth and increases nitrogen use efficiency. After addition of glauconite, an increase in potassium oxide by 0.6%, iron (III) oxide by 2.34%, and aluminium oxide by 0.41% was observed. Silicon oxide content increased by 4.9%, while the content of humic substances – by 1.1%. These changes in soil characteristics contribute to higher yields and better soil quality. The results of the study have practical significance for agriculture in improving soil characteristics, increasing yields and soil quality, and reducing dependence on chemical fertilisers

Innovation driver and county air pollution: cost-benefit analysis perspective.

Innovation is the first power to drive the county's green and low-carbon. It is crucial to explore the impact of innovation on air pollution from the perspective of counties at the bottom of the administrative division hierarchy. The article is aimed at exploring the direct impact effects, spatial spillover effects, impact mechanism pathways, non-linear relationships, and cost-benefits of innovation drive on air pollution in counties. To this end, based on the collection of county-level data from 2007 to 2020 in mainland China, the article constructs a fixed-effects model, a dynamic panel model, and a spatial Durbin model for analysis. For every 1% increase in the quantity of innovation, the county SO2 emission concentration decreases by 0.2% on average; for every 1% increase in the quality of innovation, the county SO2 emission concentration decreases by 0.3% on average. When the county innovation quantity driver increases by one standard deviation, the county SO2 concentration decreases by an average of 0.29%; when the county innovation quality driver each standard deviation increases, the county SO2 concentration is reduced by 0.33% on average. The significant entry of high-end factors, the increased frequency of regulation by the environmental protection department, and the increasing efficiency of energy use are the important mechanism pathways for innovation-driven reduction of air pollution in counties. There is no significant "(inverted) U-shaped" relationship between innovation-driven air pollution in the county samples. There is a negative spatial spillover effect of the innovation quality drive on air pollution control in all Chinese county samples. Innovation to drive the declining size of the county's sulfur dioxide can bring about one billion yuan (about 139.81 million U.S. dollars) in comprehensive economic benefits. In the coming period, county governments should build a new pattern of "blue sky and white clouds" with neighboring regions in terms of spatial agglomeration of high-end elements, green transformation and utilization of energy, and intelligent monitoring and supervision of pollution.

The role of government social media in enhancing environmental governance

ABSTRACT In light of the advancements in information and communication technologies and growing concerns over environmental issues, environmental protection agencies are increasingly turning to official social media platforms to promote environmental governance, especially in rapidly developing nations. The adoption of government social media has improved the effectiveness of environmental governance in various aspects, while also presenting some challenges. This paper summarizes findings from recent literature on the role of government social media in enhancing environmental governance. It also highlights prospective research topics on the use of social media in e-government within environmental protection departments, including investigating efficient government-citizen communication strategies using social media tools, comprehensively addressing aspects of e-government from both supply and demand perspectives, and understanding the underlying factors that contribute to the varying operating patterns of different government social media.

A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan.

Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups' levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.