Intensity-duration-frequency (IDF) curves can serve as useful tools in risk assessment of extreme environmental events. Thus, this study proposes an IDF approach for evaluating the risk of expected occurrences of extreme air pollution as measured by an air pollution index (API). Hourly data of Klang city in Malaysia from 1997 to 2016 are analyzed. For each year, a block maxima size is determined based on four different monsoon seasons. Generalized extreme value (GEV) distribution is used as a model to represent the probabilistic behavior of maximum intensity of the API, which is derived from each block. Based on the GEV model, the IDF curves are developed to estimate the extreme pollution intensities that correspond to various duration hours and return periods. Considering the IDF curves, we found that for any duration hour, the magnitude of pollution intensity tends to be high in parallel with increasing return periods. In fact, a high-intensity pollution event that poses a high risk of affecting the environment is less frequent than low-intensity pollution. In conclusion, the IDF curves provide a good basis for decision makers to assess the expected risk of extreme pollution events in the future.
Solid waste collection has been a major challenge for local council Municipal workers in urban areas where the population is growing rapidly and the need for local councils to ensure they provide service to clear solid waste in a timely manner without disrupting the existing system. This case study is a review of awareness of health and safety among municipal workers on solid waste collection. The study is an examination of such awareness in Malaysia which involves the Petaling Jaya City Council in the state of Selangor. We review the operating system of the Petaling Jaya City Council and the risks which are being exposed to the municipal workers in their daily operations. Health and Safety of municipal workers in the areas of public health, safety, and industrial hygiene has been a concern for a long time. In most cases the majority of municipal workers exposed are from the contractual aspect of outsourced labour. It is important to understand how large the area of a solid waste is and to identify, evaluate, and eliminate or control risks and hazards across the operation zone of the municipal workers which involves environmental and occupational demands. The data we find provides the latest perspectives on topics of widespread concern such contractors management, ergonomic safety, confined space, motor vehicle safety, machinery safety, fall hazard safety, electrical safety and emergency response. Management of legal requirements is also discovered to be important to ensure any organization conducting business is not in violation of any law, and if there is a change of process how is this being addressed. Finally, we look at the control measures needed to support a operation on a large scale where it will not endanger the health and safety of municipal workers when they are involved in solid waste management. The recommendation we have based on the review are to ensure there is a holistic approach and systems in place to address all the concerns that will be faced by the local municipal council in their daily operations and responsibilities at every level of the organization. Conclusion: Research material and data related to this area is limited and we hope future holistic research can be conducted to future enhance the study of health and safety effecting the municipal workers. Health and Safety management system and procedures are the key elements in ensuring a successful organization.
Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.
Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.
The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM10, SO2, CO, O3, and NO2, from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO2 (7.5%) and then SO2 (5.7%). However, we unexpectedly found that during lockdown, the PM10 concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative.
Solid waste generation has rapidly increased due to the worldwide population, urbanization, and industrialization. Solid waste management (SWM) is a significant challenge for a society that arises local issues with global consequences. Thus, solid waste management strategies to recycle waste products are promising practices that positively impact sustainable goals. Several developed countries possess excellent solid waste management strategies to recycle waste products. Developing countries face many challenges, such as municipal solid waste (MSW) sorting and handling due to high population density and economic instability. This mismanagement could further expedite harmful environmental and socioeconomic concerns. This review discusses the current solid waste management and energy recovery production in developing countries; with statistics, this review provides a comprehensive revision on energy recovery technologies such as the thermochemical and biochemical conversion of waste with economic considerations. Furthermore, the paper discusses the challenges of SWM in developing countries, including several immediate actions and future policy recommendations for improving the current status of SWM via harnessing technology. This review has the potential of helping municipalities, government authorities, researchers, and stakeholders working on MSW management to make effective decisions for improved SWM for achieving sustainable development.
Song plays a fundamental role in intraspecific communication in songbirds. The temporal and structural components of songs can vary in different habitats. These include urban habitats where anthropogenic sounds and alteration of habitat structure can significantly affect songbird vocal behavior. Urban-rural variations in song complexity, song length and syllable rate are not fully understood. In this study, using the oriental magpie-robin (Copsychus saularis) as a model, we investigated urban-rural variation in song complexity, song length, syllable rate, syllable length and inter-syllable interval. Comparing urban and rural songs from 7 countries across its natural Asiatic range (Bangladesh, India, Malaysia, Nepal, Singapore, Sri Lanka and Thailand), we found no significant differences in oriental magpie-robin song complexity. However, we found significant differences in temporal song variables between urban and rural sites. Longer songs and inter-syllable intervals in addition to slower syllable rates within urban sites contributed the most to this variance. This indicates that the urban environment may have driven production of longer and slower songs to maximize efficient transmission of important song information in urban habitats.
In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
Apart from the health aspects and the high death toll, the COVID-19 pandemic has, since its official recognition in March 2020 caused may social and economic problems. It has also led to many environmental ones. For instance, the lockdowns have led to higher levels of consumption of packaged products, and of take-away food. This paper reports on an international study on the increased consumption and subsequent changes in the amounts of waste produced since the COVID-19 pandemic. The results show that 45-48% of the respondents observed an increased consumption of packed food, fresh food, and food delivery. One of the main reasons for the increased waste generation during the lockdown was the fact that people have spent more time at home. In addition, increases of 43% and 53% in food waste and plastic packaging. Drawing from comparisons on the amount of domestic waste produced before and during the pandemic, the findings suggest that some specific types of municipal waste have visibly increased, putting additional pressure on waste management systems. This characterises one of non-intended effects of the COVID-19 pandemic. The results from this study provide useful insights to city administrations and municipal utilities on consumption patterns during emergency situations. This, in turn, may support more systemic and strategic measures to be taken, so as to curtail the increase of household waste during pandemic situations.
At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
Urban road dust contains anthropogenic components at toxic concentrations which can be hazardous to human health. A total of 36 road dust samples from five different urban areas, a commercial (CM), a high traffic (HT), a park (GR), a rail station (LRT), and a residential area (RD), were collected in Kuala Lumpur City followed by investigation into compositions, sources, and human health risks. The concentrations of trace metals in road dust and the bioaccessible fraction were determined using inductively couple plasma-mass spectrometry (ICP-MS) while ion concentrations were determined using ion chromatography (IC). The trace metal concentrations were dominated by Fe and Al with contributions of 53% and 21% to the total trace metal and ion concentrations in road dust. Another dominant metal was Zn while the dominant ion was Ca2+ with average concentrations of 314 ± 190 μg g-1 and 3470 ± 1693 μg g-1, respectively. The most bioaccessible fraction was Zn followed by the sequence Sr > Cd > Cr > Cu > Ni > Co > Mn > As > V > Pb > Fe > Al > U. The results revealed that the highest trace metal and ion concentrations in road dust and in the bioaccessible fraction were found at the LRT area. Based on the source apportionment analysis, the major source of road dust was vehicle emissions/traffic activity (47%), and for the bioaccessible fraction, the major source was soil dust (50%). For the health risk assessments, hazard quotient (HQ) and cancer risk (CR) values for each element were
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.
Urbanization is one of the leading causes of urban flooding as rapid development produces more impervious areas in cities. The application of green roofs is regarded as an effective technology to minimize the adverse effects of urban development. The stormwater management capacities of green roofs have been extensively acknowledged, and they can retain rainfall and detain runoff. Nevertheless, Malaysia has experienced few green roof applications, and only limited literature is available concerning such topics. Additionally, the incorporation of waste and recycled material in green roof designs must be considered to ensure such projects benefit the environment as well as the economy. Therefore, the construction of a green roof utilizing recycled waste materials was attempted. An extensive green roof was constructed using beach morning glory and creeping ox-eye plants as vegetation layers, along with coconut waste, i.e., coconut fiber and coconut shell, as the medium for the filter and drainage layer, respectively. According to the results, the use of recycled coconut waste materials in the green roof operations reduced the peak flow by as much as 86%, while the use of commercial materials led to a reduction of 67%.
Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects.
Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
The rapid growth of population and economy has led to an increase in urban air pollutants, greenhouse gases, energy shortages, environmental degradation, and species extinction, all of which affect ecosystems, biodiversity, and human health. Atmospheric pollution sources are divided into direct and indirect pollutants. Through analysis of the sources of pollutants, the self-functioning of different plants can be utilized to purify the air quality more effectively. Here, we explore the absorption of greenhouse gases and particulate matter in cities as well as the reduction of urban temperatures by plants based on international scientific literature on plant air pollution mitigation, according to the adsorption, dust retention, and transpiration functions of plants. At the same time, it can also reduce the occurrence of extreme weather. It is necessary to select suitable tree species for planting according to different plant functions and environmental needs. In the context of tight urban land use, the combination of vertical greening and urban architecture, through the rational use of plants, has comprehensively addressed urban air pollution. In the future, in urban construction, attention should be paid to the use of heavy plants and the protection and development of green spaces. Our review provides necessary references for future urban planning and research.
In recent years, academics have paid more attention to green finance, and public companies have reached a broad consensus on the concept of timely environmental, social, and governance (ESG) disclosure. Due to the close relationship between green finance and ESG, this presents an opportunity to determine whether green finance compels companies to actively disclose ESG. The sample for this study consists of China's non-financial A-share listed companies from 2010 to 2021, and the empirical findings demonstrate that green finance can positively influence the ESG performance of listed companies. Through an analysis of heterogeneity, this study reaches the following conclusions: state-owned enterprises, heavy pollution companies, and companies in low-carbon pilot cities perform better in terms of green finance's role in promoting ESG scoring. This study also introduces market concentration and social trust as the moderating variables, enriching the green finance research framework. Through the analysis of moderating variables, the 'black box' effect of green finance on ESG is disclosed, providing theoretical support for the government and companies to better comprehend the policy effect as well as a reference for reform and experimental promotion of green finance.
Cities are complex adaptive systems whose responses to policy initiatives emerge from feedback interactions between their parts. Urban policy makers must routinely deal with both detail and dynamic complexity, coupled with high levels of diversity, uncertainty and contingency. In such circumstances, it is difficult to generate reliable predictions of health-policy outcomes. In this paper we explore the potential for low-order system dynamics (LOSD) models to make a contribution towards meeting this challenge. By definition, LOSD models have few state variables (≤5), illustrate the non-linear effects caused by feedback and accumulation, and focus on endogenous dynamics generated within well-defined boundaries. We suggest that experience with LOSD models can help practitioners to develop an understanding of basic principles of system dynamics, giving them the ability to 'see with new eyes'. Because efforts to build a set of LOSD models can help a transdisciplinary group to develop a shared, coherent view of the problems that they seek to tackle, such models can also become the foundations of 'powerful ideas'. Powerful ideas are conceptual metaphors that provide the members of a policy-making group with the a priori shared context required for effective communication, the co-production of knowledge, and the collaborative development of effective public health policies.
Dengue, the most important mosquito-borne viral disease of humans is a recurring global health problem. In Bangladesh, dengue outbreaks are on the increase despite the efforts of government and it is not clear what the understanding of the general Dhaka population towards dengue fever is. Knowledge, attitude and practice (KAP) studies are essential guides in public health interventions. Hence, using KAP, this study aims to assess patient-perspectives with regards to factors associated with dengue, as well as investigate the associated factors between the two corporations in Dhaka. A Hospital-based cross-sectional study of 242 fever patients from two city-corporations in Dhaka (Dhaka North City Corporations, DNCC (n = 91, 37.6%) and Dhaka South City Corporation, DSCC (n = 151, 62.4%) was conducted using pre-tested KAP items. Wilcoxon's Rank Sum was used to determine the KAP by DNCC, DSCC and both corporations and multivariate Poisson regression analyses. The two corporations were analysed separately due to the differences in income distribution, concentration of slums, hospitals and clinics. The study found that more than half of the study population were knowledgeable about dengue (mean percentage scores was 52%), possess an appropriate and acceptable attitude towards the disease (69.2%), and about two thirds of the respondents (71.4%) engaged in practices towards its prevention. After adjusting for the potential cofounders, the factors associated with KAP about dengue fever varied between DNCC and DSCC; with duration of residency and use of mosquito nets were associated with knowledge in the north while income class and age were associated with knowledge and attitude in the south. In the pooled analysis (combining both corporations), knowledge of dengue was associated with good practice towards dengue fever among the respondents. The duration of residence in Dhaka (10+ years), not using mosquito nets and length of time spent in the hospital (7+ days) due to dengue, and decreased knowledge (Adjusted coefficient (β) = -0.01, 95%CI: -0.02, -0.01) were associated with attitude towards dengue in DNCC. On the other hand, middle-high income class, age (40+ years) and increased knowledge were associated with practice towards dengue in DSCC (β = 0.02, 95%CI: 0.01, 0.03). Efforts to increase knowledge about dengue fever through education by the administrations of both corporations would benefit from targeting these high-risk groups for a more sustainable outcome.
Matched MeSH terms: Cities/epidemiology; Cities/statistics & numerical data
This study was aimed at exploring the impact of religiosity on purchase intention towards counterfeit products by investigating the mediating role of consumer attitude. This study investigated religiosity as an independent variable, attitude towards counterfeit as a mediator while predicting the purchase intentions of the consumers. A self-administered questionnaire using a five-point Likert scale was used to collect data from the sample of 420 respondents who were from twin cities (Rawalpindi and Islamabad) of Pakistan. Structural equation modeling technique was used to achieve the set objectives. The results reveal a statistically significant effect of religiosity along with the significant mediating role of consumer attitude and the significant moderating role of hedonic benefits while predicting the purchase intentions of the consumers. This study also provides important insights for the researchers and the practitioners.