Gaseous Air Pollutants Research Articles

The road to zero emission shipbuilding Industry: A systematic and transdisciplinary approach to modern multi-energy shipyards

The International Maritime Organisation focuses on decarbonising the operational phase of a ship's life cycle. However, shipbuilding contributes to a significant amount of greenhouse gas emissions and air pollutants and has negative impacts on society. Holistic and transdisciplinary studies of the shipbuilding energy sector are lacking and a holistic approach is needed to discuss the potential of measures and tools to improve the shipbuilding industry with zero emissions. This study is an interdisciplinary approach to provide trends, recommendations and policies for decarbonisation of the shipping industry from a life cycle perspective. Taking into account a holistic and transdisciplinary approach, the energy sector in shipbuilding is categorised into an energy supply system, an energy economic system and an energy ecosystem, and the main disciplines for improving energy efficiency and promoting “zero emissions” for shipyards are identified, measures and tools within each discipline are proposed, and their mitigation potential and key issues for improving energy efficiency and reducing air emissions from shipyard activities are discussed. The case study highlights the economic, environmental and sustainability benefits of implementing the proposed modern energy system in an Italian shipyard. Although there is no silver bullet to eliminate air emissions in the shipbuilding industry due to the complexity, the different reduction potentials, the costs and the relationship and interaction between measures and tools, the implementation of the energy management framework can accelerate the transition to a zero-emission shipbuilding industry.

Substantial Changes in Selected Volatile Organic Compounds (VOCs) and Associations with Health Risk Assessments in Industrial Areas during the COVID-19 Pandemic

During the COVID-19 pandemic, governments in many countries worldwide, including India, imposed several restriction measures, including lockdowns, to prevent the spread of the infection. COVID-19 lockdowns led to a reduction in gaseous and particulate pollutants in ambient air. In the present study, we investigated the substantial changes in selected volatile organic compounds (VOCs) after the outbreak of the coronavirus pandemic and associations with health risk assessments in industrial areas. VOC data from 1 January 2019 to 31 December 2021 were collected from the Central Pollution Control Board (CPCB) website, to identify percentage changes in VOC levels before, during, and after COVID-19. The mean TVOC levels at all monitoring stations were 47.22 ± 30.15, 37.19 ± 37.19, and 32.81 ± 32.81 µg/m3 for 2019, 2020, and 2021, respectively. As a result, the TVOC levels gradually declined in consecutive years due to the pandemic in India. The mean TVOC levels at all monitoring stations declined from 9 to 61% during the pandemic period as compared with the pre-pandemic period. In the current study, the T/B ratio values ranged from 2.16 (PG) to 26.38 (NL), which indicated that the major pollutant contributors were traffic and non-traffic sources during the pre-pandemic period. The present findings indicated that TVOC levels had positive but low correlations with SR, BP, RF, and WD, with correlation coefficients (r) of 0.034, 0.118, 0.012, and 0.007, respectively, whereas negative correlations were observed with AT and WS, with correlation coefficients (r) of -0.168 and -0.150, respectively. The lifetime cancer risk (LCR) value for benzene was reported to be higher in children, followed by females and males, for the pre-pandemic, pandemic, and post-pandemic periods. A nationwide scale-up of this study's findings might be useful in formulating future air pollution reduction policies associated with a reduction in health risk factors. Furthermore, the present study provides baseline data for future studies on the impacts of anthropogenic activities on the air quality of a region.

African Ecocriticism, Interspecies Relationship, and Kyuka Lilymjok’s Twilight for a Vulture

Ecocriticism in Africa, often framed by the postcolonial problem, appears to have a lot of focus on the notion of environmental justice. This kind of ecocriticism, rooted in protest aesthetics, aligns with the eco-activisms of the likes of Wangari Mathai of Kenya and Ken Saro-Wiwa of Nigeria who have, in different ways, contributed immensely to the literary awareness and the physical confrontation against the institutional powers that cause environmental degradation in Africa. These environmental writers expose the business of extractive capitalism responsible for harmful practices like gas flaring, oil spills, oil bunkering, water and air pollution, tree felling, and other activities that are inimical to the biodiversity of the continent. Considering the general degradation that reduces the postcolonial societies to what Frantz Fanon calls the wretched of the earth, it could be agreed that these writers’ focus on the environmentalism of the poor is not out of place. However, this rubric deemphasizes the nonhuman implication of the environmental tragedies, leaves untapped the full potentials of those literary genres that aestheticize relations across species, and expands the vision for ecological sustainability. To change the narrative, this article uses Kyuka Lilymjok’s Twilight for a Vulture to highlight the roles of nonhuman beings of the environment by analyzing the interspecies relationships that occur when species are brought closely together in the Nigerian environment. Both humans and nonhuman species found in Nigerian environment do not exist in isolation; they relate to each other. These relationships or interactive moments open contact zones of proximity and provide opportunities for contrasting various forms of human relationship with the nonhuman world. This is not to privilege nonhumans over humans, but rather to emphasize the ways these nonhuman life forms are linked with humans.

Effect of Measurement System Configuration and Operating Conditions on 2D Material-Based Gas Sensor Sensitivity.

Gas sensors applied in real-time detection of toxic gas leakage, air pollution, and respiration patterns require a reliable test platform to evaluate their characteristics, such as sensitivity and detection limits. However, securing reliable characteristics of a gas sensor is difficult, owing to the structural difference between the gas sensor measurement platform and the difference in measurement methods. This study investigates the effect of measurement conditions and system configurations on the sensitivity of two-dimensional (2D) material-based gas sensors. Herein, we developed a testbed to evaluate the response characteristics of MoS2-based gas sensors under a NO2 gas flow, which allows variations in their system configurations. Additionally, we demonstrated that the distance between the gas inlet and the sensor and gas inlet orientation influences the sensor performance. As the distance to the 2D gas sensor surface decreased from 4 to 2 mm, the sensitivity of the sensor improved to 9.20%. Furthermore, when the gas inlet orientation was perpendicular to the gas sensor surface, the sensitivity of the sensor was the maximum (4.29%). To attain the optimum operating conditions of the MoS2-based gas sensor, the effects of measurement conditions, such as gas concentration and temperature, on the sensitivity of the gas sensor were investigated.

Air pollution associated acute respiratory inflammation and modification by GSTM1 and GSTT1 gene polymorphisms: a panel study of healthy undergraduates

Epidemiological evidence has linked air pollution with adverse respiratory outcomes, but the mechanisms underlying susceptibility to air pollution remain unclear. This study aimed to investigate the role of glutathione S-transferase (GST) polymorphism in the association between air pollution and lung function levels. A total of 75 healthy young volunteers aged 18–20 years old were recruited for six follow-up visits and examinations. Spirometry was conducted to obtain lung function parameters such as forced vital capacity (FVC), and forced expiratory volume in 1 s (FEV1). Nasal fluid concentrations of interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α), and 8-epi-prostaglandin F2α (8-epi-PGF2a) were measured using ELISA kits. Linear mixed-effect models were used to evaluate the association of air pollutants with respiratory outcomes. Additionally, polymorphisms of glutathione S-transferase mu 1 (GSTM1) and glutathione S-transferase theta 1 (GSTT1) were estimated to explore its role in the association between air pollutants and lung function. We found that short-term exposure to atmospheric particulates such as PM2.5 and PM10 can cause an increase in nasal biomarkers of inflammation, oxidative stress, and lung function, while air gaseous pollutant exposure is linked with decreased lung function, except for CO. Stratification analyses showed that an increase in nasal inflammatory cytokines caused by exposure to atmospheric particulates is more obvious in subjects with GSTM1-sufficient (GSTM1+) than GSTM1-null (GSTM1−), while elevated lung function levels due to air particles are more significant in subjects with the genotype of GSTM1− when compared to GSTM1+. As for air gaseous pollutants, decreased lung function levels caused by O3, SO2, and NO2 exposure is more manifest in subjects with the genotype of GSTM1− compared to GSTM1+. Taken together, short-term exposure to air pollutants is associated with alterations in nasal biomarkers and lung function levels in young healthy adults, and susceptible genotypes play an important mediation role in the association between exposure to air pollutants and inflammation, oxidative stress, and lung function levels.

Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach

Electric vehicles have been gaining attention as a cleaner means of transportation that is low-carbon and environmentally friendly and can reduce greenhouse gas emissions and air pollution. Despite EVs’ many advantages, widespread adoption will negatively affect the electric grid due to their random and volatile nature. Consequently, predicting the charging demand for electric vehicles is becoming a priority to maintain a steady supply of electric energy. Time series methodologies are applied to predict the charging demand: traditional and deep learning. RNN, LSTM, and transformers represent deep learning approaches, while ARIMA and SARIMA are traditional techniques. This research represents one of the first attempts to use the Transformer model for predicting EV charging demand. Predictions for 3-time steps are considered: 7 days, 30 days, and 90 days to address both short-term and long-term forecasting of EV charging load. RMSE and MAE were used to compare the model’s performance. According to the results, the Transformer outperforms the other mentioned models in terms of short-term and long-term predictions, demonstrating its ability to address time series problems, especially EV charging predictions. The proposed Transformers framework and the obtained results can be used to manage electricity grids efficiently and smoothly.

Modeling vehicle-miles of travel accounting for latent heterogeneity

Vehicle use is associated with negative externalities such as traffic congestion, air pollution, and greenhouse gas emissions. Particularly in the U.S. as a car-oriented country, vehicle use — in terms of vehicle-miles of travel (VMT) — has been on the rise and is projected to increase in the future. To curb the VMT growth and mitigate the associated externalities, policy makers can design informed strategies based on VMT predicted by vehicle use models. However, traditional vehicle use models capture merely the observed heterogeneity across vehicle decision making units (e.g., individuals) and ignore the latent or taste heterogeneity sourced in individuals' attitudes and lifestyle preferences, which may cause biased and inconsistent results that mislead implications for policy makers. To address this research gap, the present study introduces a latent class regression model, where a probabilistic multinomial logit component endogenously classifies a sample of vehicle use observations so as to be homogeneous within and heterogeneous across the classes with respect to VMT. At the same time, a finite set of linear regression equations in the number of the latent classes yields class-specific VMT. The model is estimated on a sample dataset from the State of California identifying three latent classes, verifying the hypothesis of positing vehicle use on both observed and unobserved heterogeneity. The estimation results are analyzed to infer implications of potential policies aiming at reducing VMT through increasing fuel cost and switching to telework, and to evaluate the efficiency of resource allocation to policies by targeting different classes with distinctive characteristics.

Air pollution mitigation assessment to inform Cambodia's first clean air plan

Cambodia's 16.5 million people are exposed to air pollution in excess of World Health Organisation guidelines. The Royal Government of Cambodia has regulated air pollutant emissions and concentrations since 2000, but rapid economic growth and energy consumption means air pollution continues to impact human health. In December 2021, the Ministry of Environment of Cambodia published Cambodia's first Clean Air Plan that outlines actions to reduce air pollutant emissions over the next decade. This work presents the quantitative air pollution mitigation assessment underpinning the identification and evaluation of measures included in Cambodia's Clean Air Plan. Historic emissions of particulate matter (PM2.5, black carbon, organic carbon) and gaseous (nitrogen oxides, volatile organic compounds, sulphur dioxide, ammonia, and carbon monoxide) air pollutants are quantified between 2010 and 2015, and projected to 2030 for a baseline scenario. Mitigation scenarios reflecting implementation of 14 measures included in Cambodia's Clean Air Plan were modelled, to quantify the national reduction in emissions, from which the reduction in ambient PM2.5 exposure and attributable health burdens were estimated. In 2015, the residential, transport, and waste sectors contribute the largest fraction of national total air pollutant emissions. Without emission reduction measures, air pollutant emissions could increase by between 50 and 150% in 2030 compared to 2015 levels, predominantly due to increases in transport emissions. The implementation of the 14 mitigation measures could substantially reduce emissions of all air pollutants, by between 60 and 80% in 2030 compared to the baseline. This reduction in emissions was estimated to avoid approximately 900 (95% C.I.: 530–1200) premature deaths per year in 2030 compared to the baseline scenario. In addition to improving air pollution and public health, Cambodia's Clean Air Plan could also to lead to additional benefits, including a 19% reduction in carbon dioxide emissions, simultaneously contributing to Cambodia's climate change goals.

Forecasting environmental and social benefits of adopting cleaner technologies in Indian brick manufacturing industry

Embodied carbon of new buildings can be effectively reduced by improving the manufacturing processes for energy intensive raw materials such as bricks and cement. Indian brick manufacturing industry is a major source of air pollution and greenhouse gas (GHG) emissions. This sector uses coal as a primary fuel in nearly 150,000 brick kilns, majority of which are based on energy in-efficient, fixed chimney bull-trench kiln (FCBTK) technology. Annually, the brick making in India consumes nearly 60 million tons of coal, produces almost 250 billion bricks and emits more than 150 million tons of carbon dioxide (CO2) and other GHG emissions. The pollutants produced include carbon monoxide (CO), sulfur oxides (SOx), nitrogen oxides (NOx), particulate matter (PM), methane (CH4), non-methane volatile organic compounds (NMVOC). This study provides the first comprehensive long-term (2020–2050) assessment of Indian brick kiln sector by forecasting the annual fuel demand, GHG emissions, pollutants formations and annual externality cost borne by the community. A Low Emissions Analysis Platform (LEAP) software based bottom-up simulation model has been developed for performing the assessment. Business as usual (BAU) and four alternative scenarios comprising of cleaner kiln technologies are evaluated. These technologies include zig zag kiln (ZZK), vertical shaft brick kiln (VSBK) and tunnel kiln (TK). Values for emission factors for pollutants, energy intensities of kilns, and social costs of pollutants are taken from the literature. Our results show that VSBK is the most energy efficient and environmentally friendly technology, closely followed by ZZK. For the year 2050, ZZK can save up to 16 billion dollars in externality costs and reduce coal demand by approximately 260 million tons. ZZK is not only a cleaner technology, but also has significant resemblance with conventional FCBTK technology which can be easily retrofitted to ZZK. The government and other stakeholders should promote ZZK adoption and develop suitable policy and financial instruments to address barriers to transition.

State of gaseous air pollutants and resulting health effects in Karachi, Pakistan.

Karachi, Pakistan, is a priority site for air pollution research due to high emissions of air pollutants from vehicular traffic, industrial activities, and biomass burning, as well as rapid growth in population. The objectives of this study were to investigate the levels of gaseous pollutants (NO, NO2, O3, HNO3, and SO2) in Karachi, to determine temporal and seasonal variations, to compare Karachi's air quality with other urban centers, to identify relationships with meteorological conditions, to identify source characterization, and to perform a backward-in-time trajectory analysis and a health impact assessment. Daily samples of gaseous pollutants were collected for six consecutive weeks in each of the four seasons for a year. Daily maximum concentrations of NO (90 parts per billion by volume (ppbv)), NO2 (28.1 ppbv), O3 (57.8 ppbv), and SO2 (331 ppbv) were recorded in fall, while HNO3 (9129 parts per trillion by volume (pptv)) was recorded in spring. Seasonal average concentrations were high in winter for NO (9.47 ± 7.82 ppbv), NO2 (4.84 ± 3.35 ppbv), and O3 (8.92 ± 7.65 ppbv), while HNO3 (629 ± 1316 pptv) and SO2 (20.2 ± 39.4 ppbv) were high in spring and fall, respectively. The observed SO2 seasonal average concentration in fall (20.2 ± 39.4) was 5 times higher than that in summer (3.97 ± 2.77) with the fall 24-h average (120 ppbv) exceeding the WHO daily guideline (7.64 ppbv) by a factor of about 15.7. A health impact assessment estimated an increase of 1200 and 569 deaths due to short-term exposure to SO2 in fall and spring, respectively. Chronic daily intake estimated risk per 1000 was 0.99, 0.47, 0.45, and 0.26 for SO2 in fall, NO in winter, O3 in winter, and NO2 in spring, respectively. This study confirms the effect of poor urban air quality on public health and demonstrated the influence of photochemical reactions as well as unfavorable meteorological conditions on the formation of secondary pollutants.

Unleashing the dynamic impact of tourism industry on energy consumption, economic output, and environmental quality in China: A way forward towards environmental sustainability

Tourism industry significantly enhances consumption, creates employment, and becomes a new engine of economic growth worldwide. However, tourism-led growth is attained at the cost of environmental damages, as tourism-related activities require higher energy consumption. We also included globalization, energy consumption, and economic growth to normalize the tourism industry sub-sectors environmental impacts. The ARDL bound testing and Gradual shift causality methods are used to analyze the environmental impacts of the tourism industry for the data spanning from 2001Q1 to 2019Q4. The outcomes indicate that tourism-related food and beverage services contribute to higher greenhouse gas emissions, especially N2O and CH4. However, compared with other sub-sectors, CO2 is primarily contributed by the tourism-traveling sector in the long run. Similarly, the food and beverages services also contribute highly to all air pollutants except PM2.5, while shopping and entertainment is the most significant sector contributing to N2O, CH4, and all other air pollutants in the long run. Moreover, all tourism industry sub-sectors have a significant and favorable impact on energy consumption and economic growth, whereas tourism-linked traveling consumes higher energy. Likewise, shopping and entertainment contribute more to economic growth than other sub-sectors. Interestingly, greenhouse gas emissions and air pollutants are adversely affected by globalization in the long run. Causality results reveal that all the tourism industry sub-sectors Granger cause most of the greenhouse gasses and air pollutants. The key initiatives are also provided for policy implications.

Contributions of Indoor Household Activities to Inhalation Health Risks Induced by Gaseous Air Pollutants in Hong Kong Home

To understand inhalation health risks at home, a comprehensive sampling campaign was conducted in a Hong Kong residential apartment from October to December 2019. Emissions of nine typical household activities, household background release and outdoor pollution were continuously monitored using a suite of the state-of-the-art instruments. Health risks were evaluated in each one-year exposure period. Acrolein and nitrogen dioxide (NO2) likely caused chronic non-carcinogenic risks to residents in all exposure stages. Furthermore, the health risk of the respiratory system was proved in all age groups. For the first time, several household activities were found to cause acute health risks due to exposure to formaldehyde, benzene, NO2 and acrolein in normal urban daily life. The probability distributions of cancer risks from household activities revealed that formaldehyde was the main carcinogen with an average risk of 2.00×10‑4, followed by benzene, acetaldehyde, ethylbenzene and dichlorobenzene. Among all indoor activities, incense burning was the largest contributor to total cancer risk (46%), as this source was respectively responsible for 47%, 54%, and 39% of cancer risks from formaldehyde, benzene, and acetaldehyde, followed by cooking and smoking. Notably, source-related health risk analysis showed that household background release dominated regardless of cancer risk (35.7–58.5%) or non-carcinogenic risk (57.1%). Further, the cancer risk from birth to 18 years was approximately 1.8–2.7 times that of 18~60 and 60~85 years of age. This study shed light on health risks posed by various household activities and highlighted the importance of indoor activities and household background release on acute and chronic health risks.

A Review of HKUST-1 Metal-Organic Frameworks in Gas Adsorption

The discovery of the Hong Kong University of Science and Technology-1 (HKUST-1) metal-organic framework has made a remarkable improvement in the metal-organic frameworks to achieve the goal of air polluted gas adsorption. Nowadays, HKUST-1 has become a new alternative used as an ideal platform for gas adsorption due to the open metal sites inside the structure with the large adsorption capacity and chemical tunability. The adsorption performance was related to the formation and preparation of the HKUST-1 and its effect on other fabrication parameters. The impact on the fabrications of HKUST-1, such as solvents, reactants, temperature and time, grinding process, and pressure, affected the formation of the surface area and performance of HKUST-1. Air pollution gases such as carbon dioxide (CO2), volatile organic compounds (VOCs), and methane (CH4) adsorption on HKUST-1 were studied and discussed. The structure of the HKUST-1 with the presence of the open metal sites, Lewis acid, and the benzene ring structure were the key components to act as a good adsorbate for CO2, CH4, and VOCs gas. Moreover, the incorporated post-synthesized HKUST-1, such as the added functional group, metal cations, or composite materials, could exhibit great adsorption capacities for the polluted gas instead of pristine HKUST-1.

A cost-effective IoT-based intelligent indoor air quality monitoring

Air quality monitoring has become a critical issue for human beings as it is related to living quality. Poor air quality harms human health, worsening respiratory diseases, increasing heart risks, impacting brain health, and endangering vulnerable populations. Various countries legislate air quality norms related to air monitoring and measurement standards. Nonetheless, most of the monitoring stations for air pollution are set up outdoors and monitor sources of air pollution and greenhouse gases in larger regions, neglecting the importance of indoor air quality (IAQ). Thus, monitoring the IAQ for better healthcare has become necessary. However, low-cost sensors are often associated with design compromises that hamper data reliability. Although scientists have extensively studied IAQ, the practical application of low-cost devices in indoor environments has yet to be investigated. We explored the effectiveness of a low- cost IAQ experimental monitoring device by studying the quality of the results gathered from the reference monitor unit. We hypothesized that the advanced technology and sensors would provide accurate and valid data to make the low-cost IAQ monitor feasible for widespread usage. Our goal was to provide an intelligent, movable, and cost-effective indoor air quality monitoring unit which has been designed to simulate users' indoor routes and detect air quality in real-time. Since, the main factor affecting the accuracy and validity of the measurements results from its orthogonal design. Our results revealed a low-cost monitoring device can be used with efficient design and by using sensors that are highly accurate which are low-cost for accurate and valid readings.

Modelling the Effects of Euro 7 Emission standard on Passenger Car Emissions in Germany

Road traffic represents a significant source of air pollution and greenhouse gases, particularly in urban areas. A new emission standard for passenger cars – Euro 7, coming into force in 2025 - is currently elaborated at European level to improve the air quality situation and thus the prerequisite for human well-being in the EU. The legislative proposal is expected to be published in 2022. This paper focusses on the evaluation of the effectiveness of Euro 7 standard with regard to vehicle fleet emissions in Germany. To this end, emission trend scenarios were generated for relevant exhaust gas components by applying the TREMOD model. Hereby, a strong reduction potential becomes apparent for hydrocarbon and nitrogen oxide emissions up to 2035. Assumptions laid down in this study need to be verified with real-world emission data when the new emission standard comes into force.

The Nexus Between COVID-19 Factors and Air Pollution.

There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated. The nonlinear relationship between carbon dioxide ( ) emissions, fine particulate matter and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on emissions and we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable. The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly according to the dynamic multipliers graph.

Analysis of development in solar greenhouses

The move towards a de-carbonised world, driven partly by climate science and partly by the business opportunities it offers, will need the promotion of environmentally friendly alternatives, if an acceptable stabilisation level of atmospheric carbon dioxide is to be achieved. This requires the harnessing and use of natural resources that produce no air pollution or greenhouse gases and provides comfortable coexistence of human, livestock, and plants. This study reviews the energy-using technologies based on natural resources, which are available to and applicable in the farming industry. Among these are greenhouses, which are necessary for the growth of some plants (i.e., vegetables, flowers, etc.) in severe climates. However, greenhouses require some air conditioning process to control their temperature and relative humidity to suit specific plants. To achieve this, a novel air humidifier and/or dehumidifier systems using mop fans had been designed and employed in an experimental greenhouse to evaluate its performance under a controlled environment. This device helped to reduce the energy consumption of the greenhouse whilst providing a pleasant environment for the plants inside the greenhouse. The system was designed taking into account the meteorological conditions, which affect the environment inside the greenhouse. The performance of the system was monitored over a period of time by measuring the temperature and relative humidity of the greenhouse. Results of the monitoring have shown that the system was able to provide comfortable conditions (temperatures of 16-26 oC and relative humidity of 65%) suitable for the plants grown in the experimental greenhouse. It also enabled the minimisation of temperature variation and, hence, avoided the hazard of any sudden climatic change inside the greenhouse.

Smart mobility in a smart city concept

Purpose: The purpose of this publication is to present the most important features with which the smart mobility approach is characterized. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: In a smart city, smart mobility also plays an important role in environmental protection. Modern modes of transportation, such as electric cars and urban bicycles, reduce greenhouse gas emissions and air pollution. Thus, smart mobility contributes to improving air quality and the health of residents. As part of the smart city, smart mobility is also seen as part of improving traffic safety. Smart traffic monitoring systems and rapid response to dangerous situations, such as collisions or accidents, can help minimize accidents and collisions. Smart mobility is also one of the key elements of a smart city, which contributes to improving the quality of life for city residents by increasing mobility, reducing air pollution, improving road safety and introducing innovative transportation solutions. Originality/value: Detailed analysis of all subjects related to the problems connected with the smart mobility in smart city. Keywords: smart mobility, smart city, quality of life, biking. Category of the paper: literature review.

Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method

<abstract> <p>Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m<sup>2</sup>. While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.</p> </abstract>

Assessing the Future Trajectory of the China’s Electric Vehicle Industry: A Mini-Review of the Greenhouse Gas Emissions in Beijing and Shenzhen

Electric vehicles (EVs) have emerged as a promising solution to mitigate environmental degradation caused by transportation. The increased concerns over climate change and air pollution have accelerated the adoption of EVs globally. The use of renewable energy sources such as solar and wind power to charge EVs significantly reduces the emission of harmful gases, contributing to cleaner air and a healthier environment. The transition to EVs has the potential to create a significant positive impact on the environment, reducing the carbon footprint of the transportation sector. This study will explore the potential benefits of EVs for reducing greenhouse gas emissions, air pollution, and noise pollution. Furthermore, this research will consider the impact of EVs on the energy grid and investigate the feasibility of integrating renewable energy sources to power EVs. The study will also examine the economic and social implications of EV adoption and the potential for job creation in the EV industry. The findings of this research will contribute to a better understanding of the role that EVs play in mitigating the environmental impact of transportation. This research is especially relevant as the world moves towards a more sustainable and decarbonized future. Policymakers, industry leaders, and consumers can use the insights gained from this study to make informed decisions about the future of transportation and its impact on the environment. In conclusion, this study aims to provide a comprehensive assessment of the environmental impact of EVs and their potential to create a sustainable future for transportation.