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In this work, volatile organic compounds (VOCs) in the ambient air of a secondary fiber paper mill were analyzed. For the sake of studying pollution comprehensively, four sites in the paper mill were analyzed and active sampling methods were used. Desorption was carried out with two solvents, carbon disulfide and dichloromethane. The compositions of VOCs were determined by gas chromatography-mass spectrometry (GCMS) method. The main identified substances in the four sites were as follows: (1) waste paper sorting room: alkanes, phenols, and esters; (2) papermaking workshop: benzene series, alkanes, ethers, and phenols; (3) vacuum pump outlet: benzene series and phenols; and (4) office area: benzene series and phenols. Two main toxic substances in VOCs, the benzene series and phenols, were detected in the ambient air of the paper mill. The benzene series existed in three places along the main process of the paper mill and even existed in the office area, which was far away from the production line. Additionally, phenols were detected in all sampling locations in the paper mill. [ABSTRACT FROM AUTHOR]

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health. [ABSTRACT FROM AUTHOR]

The article presents information on a study on measurements of air pollution emission factors for marine transportation. In the study, the chemical composition of the plumes of seagoing ships was investigated in a two weeks long measurement campaign in the port of Rotterdam in Holland. Some of the concerned main atmospheric components are sulphur dioxide and nitrogen dioxide.

A child's body is highly sensitive to air quality, especially regarding the concentration of particulate matter (PM). Nevertheless, due to the high cost of precision instruments, measurements of PM concentrations are rarely carried out in school areas where children spend most of their daily time. This paper presents the results of PM measurements made by a validated, low-cost university air pollution measurement system operating in a rural area near schools. An assessment of children's exposure to PM during school hours (8 a.m.–6 p.m.) at different times of the year was carried out. We show that PM10 concentrations in the air, particularly in winter, often exceeded the alert values of 50 µg m−3, posing a health risk to children, especially when children exercise outside the school building. We also calculated the rate and total PM10 deposition in the respiratory tract during various physical activities performed in clean and polluted air. Monitoring actual PM10 concentrations as presented in this paper, using a low cost sensors, offer school authorities and teachers an opportunity to reduce health risks for children. This can be achieved by adjusting the duration and exercise intensity of children's outdoor physical activities according to the measured air quality. [ABSTRACT FROM AUTHOR]

In today's urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia's capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. [ABSTRACT FROM AUTHOR]

Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C6H6, HCHO, SO2; and particulate matter: PM1, PM2.5, PM10 demonstrate the validity of such measures. This research was carried out in the area of one of Poland's key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called "green cities". [ABSTRACT FROM AUTHOR]

The article presents a study which discusses the application of a regional Community Multi-scale Air Quality (CMAQ) modelling system at a hemispheric scale to investigate the atmospheric circulation of an air pollutant such as mercury. It relates that aircraft and ground measurements were performed to determine total gaseous mercury (TGM) concentration in the atmosphere. It mentions that the study also make used of two simulation methods.

The article presents a study which evaluates the ability of COSMO-ART, which is an online-coupled, regional-scale chemistry transport model, to measure air pollutants in Europe. It relates that the chemistry transport model is based on the combination of numerical weather prediction model of the Consortium for Small Scale Modeling (COSMO) with an extension for Aerosols and Reactive Trace (ART) gases. It mentions that the combination allows a consistent treatment of all air pollutants.

The article presents a study which evaluates the air ion spectrometers of Airel Ltd. from the European Integrated Project on Aerosol Cloud Climate Air Quality Interactions (EUCAARI). The spectrometers include Air Ion Spectrometers (AIS), Neural Cluster and Air Ion Spectrometers (NAIS) and Airborne NAIS (ANAIS). Reference instruments used in the study are highlighted which include Differential/Scanning Mobility Particle Sizer (DMPS), an Ion-DMPS and a Balanced Scanning Mobility Analyzer (BSMA).

Successful air quality management and control not only requires measurements of air pollution levels. It also requires information on the sources of air pollution, and their relative magnitudes and importance, to plan and enact cost-effective control measures. This paper provides an important breakthrough towards the wider and more comprehensive use of source apportionment via low-cost techniques. Low-cost sensor measurements, along with the statistical methods of Positive Matrix Factorization (PMF) and k-means clustering, were able to successfully pinpoint and quantify the main sources of pollution in three regulatory important sites (a construction site, a quarry and a roadside). The anticipated levels of pollution, which were dependent on meteorological conditions and temporal variations, were assessed. The analysis provides information crucial for successful air quality management and control at a significantly lower cost than previously achieved. The strengths and weaknesses of the methodologies used are illustrated and discussed. [ABSTRACT FROM AUTHOR]

Large-scale data collection by means of wireless sensor network and Internet-of-Things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this paper, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm—built upon belief-propagation principles—that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against the state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing. [ABSTRACT FROM PUBLISHER]

The interdependence between air quality, human health and the state of the environment has prompted the development of research on causes, control and improvement of existing pollutants in the air. This paper addresses the problem of air pollution by PM2.5 and PM10 in particulate matter. There was draws attention to solutions to protect air against existing PM2.5 and PM10. Measurements of PM2.5 and PM10 concentrations in selected twenty control points on the campus of the Białystok University of Technology were discussed and analysed. On the basis of the obtained results, an assessment of the air quality in the area in question was performed. Slight hourly fluctuations in the concentration of particulate matter in the air were observed, higher in the morning and evening hours. On 15 March and 16 March, daily exceedances of the permissible PM10 and PM2.5 concentrations were recorded at both the Białystok University of Technology campus and the air quality monitoring stations in Białystok. Comparing the influence of meteorological conditions on PM10 and PM2.5 concentrations, faint correlations were found for temperature and wind speed. As temperature increased, particulate matter concentrations decreased. Low wind velocities corresponded with exceedances of the permissible daily concentrations of PM10 and PM2.5 in the air. [ABSTRACT FROM AUTHOR]

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections. [ABSTRACT FROM AUTHOR]

Currently, the presence and levels of air pollution in most stingless bee farms are not measured periodically nor there are proper monitoring levels that can be attributed to hive productivity. Long-term measurement and monitoring of air pollution for hive productivity and honey yield have not been performed. Therefore, this research proposes long-term and real-time air pollution measurement and monitoring techniques that are important for correlation analysis and parameter modeling. By using air pollution detection sensors through a wireless sensor network topology in stingless bee farms, details of pollutant presence and levels will be available in real-time over a long period of time for hive productivity correlation analysis. With such deployment using IoT -based systems, data can be easily accessed from anywhere thus ensuring data continuity. This paper describes a preliminary study on the design, development, and testing of real-time air pollution measurement and monitoring systems capable of determining the health status of stingless bee nests in mixed livestock crop farms. The goal of this research is to help beekeepers/users maintain control and be able to take quick action on the hive from the information obtained. [ABSTRACT FROM AUTHOR]

Stand-off detection of airborne chemical compounds has proven to be a useful method that is gaining popularity following technical progress. There are obvious advantages compared to in situ measurements when it comes to the security aspect and the ability to measure at locations otherwise hard to reach. However, an inherent limitation in many of the stand-off detection techniques lies in the fact that the measured signal from a chemical depends nonlinearly on the distance to the detector. Furthermore, the measured signal describes the summation of the responses from all chemicals spatially distributed in the line of sight of the instrument. In other words, the three dimensional extension of the chemical plume is converted into a two-dimensional image. Not only is important geometric information per se lost in this process, but the measured signal strength itself depends on the unknown plume distribution which implies that the interpretation of the observation data suffers from significant uncertainty. In this paper we investigate different and novel approaches to reconstruct the original three-dimensional distribution and concentration of the plume by implementation of atmospheric dispersion models and numerical retrieval methods. In particular our method does not require a priori assumptions on the three-dimensional distribution of the plume. We also strongly advocate the use of proper constraints to avoid unphysical solutions being derived (or post-process 'adjustments' to correct unphysical solutions). By applying such a reconstruction method, both improved and additional information is obtained from the original observation data, providing important intelligence to the analysts and decision makers. [ABSTRACT FROM AUTHOR]

Atmospheric particulate matter smaller than 2.5 µm in diameter (PM 2.5) has a negative impact on public health, the environment, and Earth's climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM 2.5 concentrations at a global scale. Existing PM 2.5 measurement infrastructure provides broad PM 2.5 sampling coverage but does not adequately characterize community-level air pollution at high temporal resolution. This motivates the development of low-cost sensors which can be more practically deployed in spatial and temporal configurations currently lacking proper characterization. Wendt et al. (2019) described the development and validation of a first-generation device for low-cost measurement of AOD and PM 2.5 : the Aerosol Mass and Optical Depth (AMODv1) sampler. Ford et al. (2019) describe a citizen-science field deployment of the AMODv1 device. In this paper, we present an updated version of the AMOD, known as AMODv2, featuring design improvements and extended validation to address the limitations of the AMODv1 work. The AMODv2 measures AOD and PM 2.5 at 20 min time intervals. The sampler includes a motorized Sun tracking system alongside a set of four optically filtered photodiodes for semicontinuous, multiwavelength (current version at 440, 500, 675, and 870 nm) AOD sampling. Also included are a Plantower PMS5003 sensor for time-resolved optical PM 2.5 measurements and a pump/cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. AMODv2 samples are configured using a smartphone application, and sample data are made available via data streaming to a companion website (https://csu-ceams.com/ , last access: 16 July 2021). We present the results of a 9 d AOD validation campaign where AMODv2 units were co-located with an AERONET (Aerosol Robotics Network) instrument as the reference method at AOD levels ranging from 0.02 ± 0.01 to 1.59 ± 0.01. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.04, 0.06, 0.03, and 0.03 AOD units at 440, 500, 675, and 870 nm, respectively. We derived empirical relationships relating the reference AOD level to AMODv2 instrument error and found that the mean absolute error in the AMODv2 deviated by less than 0.01 AOD units between clear days and elevated-AOD days and across all wavelengths. We identified bias from individual units, particularly due to calibration drift, as the primary source of error between AMODv2s and reference units. In a test of 15-month calibration stability performed on 16 AMOD units, we observed median changes to calibration constant values of - 7.14 %, - 9.64 %, - 0.75 %, and - 2.80 % at 440, 500, 675, and 870 nm, respectively. We propose annual recalibration to mitigate potential errors from calibration drift. We conducted a trial deployment to assess the reliability and mechanical robustness of AMODv2 units. We found that 75 % of attempted samples were successfully completed in rooftop laboratory testing. We identify several failure modes in the laboratory testing and describe design changes that we have since implemented to reduce failures. We demonstrate that the AMODv2 is an accurate, stable, and low-cost platform for air pollution measurement. We describe how the AMODv2 can be implemented in spatial citizen-science networks where reference-grade sensors are economically impractical and low-cost sensors lack accuracy and stability. [ABSTRACT FROM AUTHOR]

This paper presents the development of a compact battery-powered system that monitors the carbon dioxide level, temperature, relative humidity, absolute pressure, and intensity of light in indoor spaces, and that sends the measurement data using the existent wireless infrastructure based on the IEEE 802.11 b/g standards. The resulted device's characteristics and performance are comparable with the ones provided by recognized solutions, such as ZigBee-based sensor nodes. By combining Wi-Fi connectivity with ambient sensors, this solution can be used for the remote gathering and further processing of measurement data. Testing revealed that the system can operate continuously for up to three years on a single 3 V small battery. [ABSTRACT FROM PUBLISHER]

This article presents abstracts of several articles related to economics. The abstract of the article "Formal Structure of Majority Decision," by Y. Murakami tries to investigate, by introducing some theorems on three-valued logic, logical conditions for a more general class of majority decision including representative system in its widest sense. The abstract of the article "The Effect of Air Pollution on House Values," by H.O. Nourse reports that air pollution will have a detrimental effect on residential property values. Economists, real estate appraisers, and urban land economists, have made general statements about the loss of residential property value resulting from air pollution and other nuisances. These judgments, however, have been a result of general observation of the real estate market, rather than any attempt to statistically estimate the extent of damage caused by air pollution. The model of urban house values used for the analysis is an adaptation of the matrix model. Both cross-section and time-series approaches to measurement are developed.

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

The assessment of greenhouse gases emitted to and removed from the atmosphere is high on the international political and scientific agendas. Growing international concern and cooperation regarding the climate change problem have increased the need for policy-oriented solutions to the issue of uncertainty in, and related to, inventories of greenhouse gas (GHG) emissions. The approaches to addressing uncertainty discussed in this Special Issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, systems analytical perspective-a perspective that seeks to strengthen the usefulness of national inventories under a compliance and/or global monitoring and reporting framework. These approaches demonstrate the benefits of including inventory uncertainty in policy analyses. The authors of the contributed papers show that considering uncertainty helps avoid situations that can, for example, create a false sense of certainty or lead to invalid views of subsystems. This may eventually prevent related errors from showing up in analyses. However, considering uncertainty does not come for free. Proper treatment of uncertainty is costly and demanding because it forces us to make the step from 'simple to complex' and only then to discuss potential simplifications. Finally, comprehensive treatment of uncertainty does not offer policymakers quick and easy solutions. The authors of the papers in this Special Issue do, however, agree that uncertainty analysis must be a key component of national GHG inventory analysis. Uncertainty analysis helps to provide a greater understanding and better science helps us to reduce and deal with uncertainty. By recognizing the importance of identifying and quantifying uncertainties, great strides can be made in ongoing discussions regarding GHG inventories and accounting for climate change. The 17 papers in this Special Issue deal with many aspects of analyzing and dealing with uncertainty in emissions estimates. [ABSTRACT FROM AUTHOR]

This paper presents a method for constructing a membership function (MF) for the fuzzy sets that expert systems deal with. This paper introduces a Bezier curve-based mechanism for constructing MFs of convex normal fuzzy sets. The mechanism can fit any given data set with a minimum level of discrepancy. In the absence of data, the mechanism can be intuitively manipulated by the user to construct MFs with the desired shape. MFs have been developed using the proposed mechanism for urban vehicular exhaust emission modeling. It has been observed that all meteorological and vehicular parameters have either S-shaped MFs or Z-shaped MFs. Gaussian MF has been mostly applied for modeling air quality. The present study explored the application of fuzzy MF to analyze air pollution data from vehicular emission. The study reveals that S-shaped and Z-shaped MF can be used in addition to Gaussian MF. [ABSTRACT FROM AUTHOR]

This paper discusses challenges arising in the design of networks for monitoring extreme values over the domain of a random environmental space-time field { X ij } i = 1, . . . , I denoting site and j = 1, . . . denoting time (e.g. hour). The field of extremes for time span r over site domain i = 1, . . . , I is given by $$\{Y_{i(r+1)}=\max_{j=k}^{k+n-1} X_{ij}\}$$ for k = 1 + rn, r = 0, . . . ,. Such networks must not only measure extremes at the monitored sites but also enable their prediction at the non-monitored ones. Designing such a network poses special challenges that do not seem to have been generally recognized. One of these problems is the loss of spatial dependence between site responses in going from the environmental process to the field of extremes it generates. In particular we show empirically that the intersite covariance Cov( Y i( r+1), Y i′( r+1)) can generally decline toward zero as r increases, for site pairs i ≠ i′. Thus the measured extreme values may not predict the unmeasured ones very precisely. Consequently high levels of pollution exposure of a sensitive group (e.g. school children) located between monitored sites may be overlooked. This potential deficiency raises concerns about the adequacy of air pollution monitoring networks whose primary role is the detection of noncompliance with air quality standards based on extremes designed to protect human health. The need to monitor for noncompliance and thereby protect human health, points to other issues. How well do networks designed to monitor the field monitor their fields of extremes? What criterion should be used to select prospective monitoring sites when setting up or adding to a network? As the paper demonstrates by assessing an existing network, the answer to the first question is not well, at least in the case considered. To the second, the paper suggests a variety of plausible answers but shows through a simulation study, that they can lead to different optimum designs. The paper offers an approach that circumvents the dilemma posed by the answer to the second question. That approach models the field of extremes (suitably transformed) by a multivariate Gaussian-Inverse Wishart hierarchical Bayesian distribution. The adequacy of this model is empirically assessed in an application by finding the relative coverage frequency of the predictive credibility ellipsoid implied by its posterior distribution. The favorable results obtained suggest this posterior adequately describes that (transformed) field. Hence it can form the basis for designing an appropriate network. Its use is demonstrated by a hypothetical extension of an existing monitoring network. That foundation in turn enables a network to be designed of sufficient density (relative to cost) to serve its regulatory purpose. [ABSTRACT FROM AUTHOR]

Purpose - Analysis and comparison of three existing indicators of the air quality: the American air quality index, the French Atmo, and the Italian Indice di Qualità dell'Aria. Design/methodology/approach - International general and organic regulations to control air quality do not exist yet Consequently many countries have independently implemented specific indicators to monitor the air pollution and then alert people of resulting health risks. The paper focuses on three of them. Each one is independently presented showing the peculiarities. Therefore, these indicators are compared to identify the features they have in common, as well as those that set them apart, and to figure out which are either restrictive or permissive, and what are their qualities and drawbacks. Findings - The three mentioned indicators convert the real health risk due to air pollution into numerical information, in different ways. Doing this, they carry out some simplifications or assumptions, which can be questionable. The main difficulty is to understand if the indicators aggregate the different pollutant concentrations consistently with the real effects on human health. Research limitations/implications - This paper analyses only three specific indicators of the air quality, selected among the existing ones. Practical implications - Indicators should carefully be analysed to understand if they properly represents the real effects of pollutants on human health. The most critical aspect to consider is the aggregation of the different pollutant concentrations in one information. Originality/value - This paper analyses the efficacy of representation of some air quality indicators. It discusses if indicators aggregation is consistent with the real effects on human health. [ABSTRACT FROM AUTHOR]

SnO2 sensors are widely used for the detection of air contaminants such as CO. Nevertheless, their application encounters several problems, mainly the effect of interfering gases. The low selectivity is, in fact, a well-known problem of these sensors. Moreover, the high operating temperature of metal oxide sensors implies, in general, high power consumption. In this paper, we present a study aimed at the selection of an appropriate measurement technique for detection of CO for indoor applications (lower threshold 100 ppm), in the presence of high concentrations of ethanol (up to 1000 ppm), by using only one sensor. Moreover, the paper aims at developing portable CO detectors that are very small, low power, and could be battery operated. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of increasing air pollution monitoring stations in Tehran city for efficient spatial prediction. As the data are multivariate and skewed, we introduce two multivariate skew models through developing the univariate skew Gaussian random field proposed by Zareifard and Jafari Khaledi [21]. These models provide extensions of the linear model of coregionalization for non-Gaussian data. In the Bayesian framework, the optimal network design is found based on the maximum entropy criterion. A Markov chain Monte Carlo algorithm is developed to implement posterior inference. Finally, the applicability of two proposed models is demonstrated by analyzing an air pollution data set. [ABSTRACT FROM PUBLISHER]

In this study, the authors report the first greenhouse gas emission inventory of Morelos, a state in central Mexico, in which the emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have been identified using the Intergovernmental Panel on Climate Change (IPCC) methodology. Greenhouse gas (GHG) emissions were estimated as CO2equivalents (CO2eq) for the years 2005, 2007, and 2009, with 2005 being treated as the base year. The percentage contributions from each category to the CO2eq emissions in the base year were as follows: 38% from energy, 30% from industrial processes, 23% from waste, 5% from agriculture, and 4% from land use/land use change and forestry (LULUCF). As observed in other state inventories in Mexico, road transportation is the main source of CO2emissions, wastewater handling and solid waste disposal are the main sources of CH4emissions, and agricultural soils are the source of the most significant N2O emissions. The information reported in this inventory identifies the main emission sources. Based on these results, the government can propose public policies specifically designed for the state of Morelos to establish GHG mitigation strategies in the near future.Implications: In this paper, the emissions of greenhouse gases that are generated in the state of Morelos in central Mexico were calculated. The authors calculated carbon dioxide, methane, and nitrous oxide gases emitted from the categories energy, waste, industrial processes, agriculture, and LULUCF. These emissions correspond to 1.24% of the total issued nationwide. These first results will be the basis for mitigation and adaptation strategies against climate change, as well as for establishing public policies in the study area. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

On-road exhaust emissions of a Euro 5 factory bi-fuel CNG/gasoline light-duty vehicle equipped with the TWC were assessed considering the Real Driving Emissions (RDE) guidelines. The vehicle was equipped with a Portable Emission Measurement System (PEMS) that enabled the measurement of THC, CO, NO x , CO 2, and CH 4 . With respect to the characteristics of the vehicle, the appropriate Worldwide Harmonized Light-Duty Vehicle Test Cycles (WLTC) were selected and based on the requirements of the RDE legislation a suitable route was conceived. In addition to the moderate RDE-based route, an extended RDE-based route was also determined. The vehicle was driven along each defined route twice, once with each individual fuel option and with a fully warm vehicle. RDE routes feature a multitude of new driving patterns that are significantly different to those encountered in the NEDC. However, as these driving patterns can greatly influence the cumulative emissions an insight in to local time trace phenomena is crucial to understand, reason and to possibly reduce the cumulative emissions. Original contributions of this paper comprise analyses of the RDE-LDV local time resolved driving emissions phenomena of a CNG-powered vehicle that are benchmarked against the ones measured under the use of gasoline in the same vehicle and under similar operating conditions to reason emission trends through driving patterns and powertrain parameters and exposing the strong cold-start independent interference of CO and N 2 O infrared absorption bands in the non-dispersive infrared (NDIR) analyzer. The paper provides experimental evidence on this interference, which significantly influences on the readings of CO emissions. The paper further provides hypotheses why CO and N 2 O interference is more pronounced when using CNG in LDVs and supports these hypotheses by PEMS tests. The study reveals that the vehicle's NO x real-world emission values of both conceived RDE-based routes when using both fuels are within Euro 5 and type-approval limits. Additionally, the THC and the NMHC emissions of both RDE-based routes using both fuels are within the Euro 5 limits indicating reasonable CH 4 emissions. Notable increases above the type-approval and Euro 5 limits appeared in the CO emissions profile when using gasoline, while the CO 2 emissions profile expectedly also exceeded the type-approval specifications. [ABSTRACT FROM AUTHOR]

In numerous developing nations, the pervasive practice of crop residue incineration is a principal contributor to atmospheric contamination in agricultural operations. This study examines the repercussions of such biomass combustion on air quality during the autumnal harvest season, utilizing data acquired from satellite-based remote sensing of fire events and air pollution measurements. Employing wind direction information alongside difference-in-difference and fixed-effects methodologies, this investigation rectifies estimation inaccuracies stemming from the non-random distribution of combustion occurrences. The empirical findings reveal that agricultural residue burning precipitates an elevation in average PM2.5 and PM10 concentrations by approximately 27 and 22 μg/m3 during the autumnal incineration period, respectively. Furthermore, air pollution attributed to residue burning in prominent grain-producing regions exceeds the national average by approximately 40%. By integrating economic paradigms into agri-environmental inquiries, this study offers novel insights and substantiation of the environmental expenditures engendered by crop residue burning, juxtaposed with extant meteorological and ecological research findings. [ABSTRACT FROM AUTHOR]

Vertical profiles of atmospheric pollutants, acquired by unmanned aerial vehicles (UAVs, known as drones), represent a new type of observation that can help to fill the existing observation gap in the planetary boundary layer. In this article, the first study of assimilating air pollutant observations from drones is presented to evaluate the impact on local air quality analysis. The study uses the high-resolution air quality model EURAD-IM (EURopean Air pollution Dispersion – Inverse Model), including the four-dimensional variational data assimilation system (4D-var), to perform the assimilation of ozone (O3) and nitrogen oxide (NO) vertical profiles. 4D-var takes advantage of the inverse technique and allows for simultaneous adjustments of initial values and emissions rates. The drone data was collected during the MesSBAR (Automatisierte luftgestützte Mes sung der S chadstoff B elastung in der erdnahen A tmosphäre in urbanen R äumen / Automated airborne measurement of air pollution levels in the near earth atmosphere in urban areas) field campaign, which was conducted on 22–23 September 2021 in Wesseling, Germany. The two-day analyses reveal that the 4D-var assimilation of high-resolution drone measurements has a beneficial impact on the representation of regional air quality in the model. On both days, a significant improvement in the vertical distribution of O3 and NO is noticed in the analysis compared to the reference simulation without data assimilation. Moreover, the validation against independent observations shows an overall improvement in the bias, root-mean-square error, and correlation for O3, NO, and NO2 (nitrogen dioxide) ground concentrations at the measurement site as well as in the surrounding region. Furthermore, the assimilation allows for the deduction of emission correction factors in the grid cells surrounding the measurement site, which significantly contribute to the observed improvement in the analysis. [ABSTRACT FROM AUTHOR]

In this paper we have presented detailed results of air pollution over Shkodra region. We have obtained measurement results on number and mass concentration of particulate matter. Measurement campaigns were carried out in several areas; urban centre of Shkodra city, rural areas around this city, places near the perimeter of Shkodra Lake, seashore places of Velipoja (Adriatic Sea), and nearest mountain areas. The principal goal of this study is evaluation of air pollution situation in Shkodra region, and the determination of principal sources of particulate matter in this region. Monitoring results indicate the fact that principal sources in the Shkodra region are; traffic from the nearest roads, combustion activities from Shkodra city and surrounding residential rural centers, combustion activities from residential and commercial activities around the Shkodra Lake and Velipoja beach, biomass burning in rural areas, sea salt from the Adriatic Sea, and residential and touristic activities in mountain areas. Higher concentrations of particulate matter were obtained in urban centre of Shkodra city, whilst lower values were obtained in mountain areas. [ABSTRACT FROM AUTHOR]

The provinces of Saskatchewan, Alberta, and British Columbia are major oil- and gas-producing regions in western Canada. With increasing oil and gas production activities, there has been a growing concern of the effect of oil and gas industry emissions on health. Nevertheless, lack of proper tools to estimate the exposure to these emissions has been a hindrance to epidemiological studies and risk assessment. This paper presents a spatiotemporal modeling approach to estimating ambient sulfur dioxide (SO2) levels based on environmental monitoring data ( N = 10,295), which were collected at rural sites (591 per month on average) of this region from June 1, 2001 to May 31, 2002. Based on the model, illustrative maps consistently revealed high and low SO2 concentration sub-regions. The sub-regions with elevated SO2 concentrations had increased levels during the winter months from December 2001 to March 2002 and then decreased during the spring of 2002. This statistical modeling approach may help researchers estimate the SO2 levels within the study area for their epidemiological studies or risk assessment. [ABSTRACT FROM AUTHOR]

This paper explores the issues in estimating the greenhouse gas (GHG) emissions from the tourism industry and related activity in Australia. The scope of tourism consists of the economic activities defined as “tourism characteristic” and “tourism connected” as defined in the Australian Tourism Satellite Account (TSA). Two approaches are employed and contrasted - a “production approach” and an “expenditure approach”. Depending on the approach, tourism contributes between 3.9% and 5.3% of total industry GHG in Australia. The rationale for each approach is explained. The GHG emissions have been estimated for 2003-2004, the latest year for which detailed industry GHG emissions data are available in a form suitable for this type of analysis. Tourism's GHG emissions are compared with other industries in the Australian economy. The policy implications of the results are discussed. It should be possible to adopt a broadly similar method for any destination with TSA - enabling tourism stakeholders to play an informed role in assessing appropriate and effective climate change mitigation strategies for their destination. [ABSTRACT FROM AUTHOR]

Microelectromechanical-system (MEMS) metal-oxide gas sensors have reached a mature stage, which makes mass market applications in the automotive area possible. In contrast to the already established flap-control system, which controls the access of (combustion) gases from outside the vehicle to the car cabin, the system studied here detects odor events created within the car cabin. The events under study have been cigarette smoke, fast-food odor, manure, and bioeffluents (flatulence). As the reference cannot be a "simple analytical measurement," a human test panel for assessing the hedonic impression on a scale from 0 to 5 is used as reference. The technical system is a MEMS metal-oxide-sensor array consisting of three different sensors. The data-evaluation approach used here is combining the human-sensory data and the MEMS sensor data. The task is performed by the combination of two independent algorithms, where one is related to the normalized conductance and the other to signal variance. Using a combined approach has the advantage that "false" events are suppressed. After the algorithm was successfully transferred onto a microcontroller, real-life data were recorded and classified. Several practical examples are given in this paper. The overall gas-sensor system reaches good accordance with the human-sensory impression, which is represented by air quality levels. This enables the design of a demand-controlled ventilation system [ABSTRACT FROM PUBLISHER]

This paper describes a broadband cavity ring-down spectrometer and its deployment during the 2002 North Atlantic Marine Boundary Layer Experiment (NAMBLEX) to measure ambient concentrations of NO3, N2O5, I2 and OIO at the Mace Head Atmospheric Research Station, Co. Galway, Ireland. The effective absorption path lengths accessible with the spectrometer generally exceeded 10 km, enabling sensitive localised "point" measurements of atmospheric absorbers to be made adjacent to the other instruments monitoring chemically related species at the same site. For the majority of observations, the spectrometer was used in an open path configuration thereby avoiding surface losses of reactive species. A subset of observations targeted the N2O5 molecule by detecting the additional NO3 formed by the thermal dissociation of N2O5. In all cases the concentrations of the atmospheric absorbers were retrieved by fitting the differential structure in the broadband cavity ringdown spectra using a methodology adapted from long path differential optical absorption spectroscopy. The uncertainty of the retrieval depends crucially on the correct treatment and fitting of the absorption bands due to water vapour, a topic that is discussed in the context of analysing broadband cavity ringdown spectra. The quality of the measurements and the retrieval method are illustrated with representative spectra acquired during NAMBLEX in spectral regions around 660 nm (NO3 and N2O5) and 570 nm (I2 and OIO). Typical detection limits were 1 pptv for NO3 in an integration time of 100 s, 4 pptv for OIO and 20 pptv for I2 in an integration time of 10 min. Additionally, the concentrations of atmospheric water vapour and the aerosol optical extinction were retrieved in both spectral regions. A companion paper in this issue presents the time series of the measurements and discusses their significance for understanding the variability of short lived nitrogen and iodine compounds in the marine boundary layer. [ABSTRACT FROM AUTHOR]

The environments in which we live, work, breathe, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality, measurements must be fast (real-time), scalable, and reliable (with known accuracy, precision, and stability over time). Low-cost AQ sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform stakeholders about pollution sources and inspire policy makers to address environmental justice air quality issues. In this paper, initial results obtained with a recently developed low-cost air quality sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical sensor measurements of CO, NO, NO2, and O3. Through the use of High Dimensional Model Representation (HDMR), we show that interference effects derived from changing environmental conditions and the ambient-gas concentration mix encountered at an urban neighborhood site can be effectively modelled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, improving the credibility of air pollutant measurements made with these sensors. [ABSTRACT FROM AUTHOR]

This study applies a receptor model to quantify source contributions to ambient concentration of polychlorinated dibenzo- p -dioxins and dibenzofurans (PCDD/Fs) and inhalation cancer risk in Taipei from 2003 through 2009. Seventeen PCDD/F congeners were used in the effective variance solution to the Chemical Mass Balance equations to estimate source-specific mass contributions and inhalation risks. The average total PCDD/F concentration was 0.611 pg/Nm 3 (0.036 pg I-TEQ/Nm 3 ). Traffic emissions contributed the most to the PCDD/F concentration (55.7%), followed by waste incinerators (18.6%) and joss stick burning (9.6%). For the inhalation cancer risk, the average was 1.1 × 10 −6 with traffic, waste incinerators, and joss paper burning as the main contributors (67.3%, 19.4%, and 6.3%, respectively). The mass and risk contributions of waste incinerators decreased significantly from 2003 to 2009 and were higher at downwind sites than at upwind sites. Reducing PCDD/F emissions from traffic and waste incinerators would provide the greatest health benefit. Policies that reduce the uncontrolled burning of joss stick and joss paper also need to be implemented. [ABSTRACT FROM AUTHOR]

The paper deals with the possibility of appropriate measurement and evaluation of emissions of nitrogen oxides. Development of exhaust systems which captures the solid particles emitted from engine, lost an objective assessment of the emission status of the diesel engine of agricultural tractor. Therefore, it is necessary to find a new method of measuring and quantifying the emission state of the diesel engine by measuring emissions, which should be economic and time-saving, but especially universal and sufficiently precise. The selected method should also provide sufficient information on such emissions that are subject to approval but they are not controlled during periodic checks. [ABSTRACT FROM AUTHOR]

Image classification techniques have been applied to detect higher pollution episodes in modelled air pollution data. These techniques are widely used in video processing to find patterns in videos. An attempt for the first time has been made to apply these techniques by considering air pollution as continuous video frames as the spatio-temporal changes in the pollution are linked to its previous state of the atmosphere. The applicability of these techniques has been tested over Northern Italy to detect ozone pollution episodes in year 2004 using model simulated concentrations. The methods tested in this paper are pixel, block-based, histogram, pertinent pixel and twin-comparison method. While these techniques have some kind of merits and demerits, a modified pertinent pixel comparison algorithm has been proposed to detect pollution episodes. The proposed method has been validated to detect PM10 episodes over Milan metropolitan area during 2 months in 2008 and is able to detect PM10 episodic events as well as non-events. This method provides a single binary index that can be applied by the air quality modellers and decision makers to determine the pollution episode over a given domain. [ABSTRACT FROM AUTHOR]

The real-time monitoring of the air pollution with multiple sources is of great significance for pollution control and environmental protection. In this paper, we presented a study of terahertz time-domain spectroscopy (THz-TDS) as a direct tool for monitoring the component and content of PM2.5 in atmosphere. Due to the THz absorption, the intensities of the peaks in THz-TDS decreased with the augment of PM2.5 and were proportional to the PM2.5 content. The ratio of absorbance A to PM2.5 reflected a basically unchanged tendency, indicating the little change of principal elements under the pollution degree. In the highpollution condition, a lot of SO2 from vehicle and factory was emitted into air. The elements, such as S and O from anions, had a stronger absorption effect in THz range. Based on the absorbance spectra, the absorption tendencies with PM2.5 over the whole range were validated by principal component analysis and the quantitative model with a high correlation was built by using back propagation artificial neural network. BPANN model improved the precision of linear fitting between peak intensities and PM2.5. The research demonstrates that THz-TDS is a promising tool for fast, direct, and reliable monitoring in environmental applications. [ABSTRACT FROM AUTHOR]

The paper describes a field exposure system involving the application of growing chambers, ventilated with filtered or nonfiltered ambient air The diffusive resistance of primary leaves of beans (Phaseolus vulgaris L.), growing in medium with ammonium or nitrate as nitrogen source, has been used as indicator for low level air pollution, which does not produce visible leaf injury. In severely polluted atmosphere an addition of sodium chloride affected inversely the degree of injury. [ABSTRACT FROM AUTHOR]

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by - 9.7 % for CH 4 , - 21 % for CO, and - 18 % for O 3. Results for CH 4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O 3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons. [ABSTRACT FROM AUTHOR]

The method of modeling and simulating different phenomena in various fields of scientific knowledge is an essential part of many branches of science, each of which has specific types of models. Using mathematical methods, these models try to simulate a real situation by means of numerical analysis that describes the relationships between the characteristic quantities of air pollution phenomena, such as emissions, meteorology atmospheric concentrations of pollutants, deposition, and other factors. Air and water pollution measurements give information about the environmental concentrations of pollutants scattered by different sources but can only describe the phenomena involved qualitatively, at specific locations and times, without identifying the causes that lead to pollution problems. However, models of air and groundwater pollution can provide a deterministic description of the problems of air or water quality, including an analysis of the factors and causes of the pollution phenomenon (emission sources, meteorological processes and chemical and physical changes), and some guidance for implementing measures to mitigate these phenomena of environmental pollution. Due to the ability of these methods to establish the relative importance of relevant processes, they play an important role in current day science. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by professor J. Van Leeuwen and colleagues on the environmental implication of the release of ship ballast water, one on the air quality monitoring network in Granada, Spain, and one regarding air quality issues that were raised in the International Ozone Association (IOA) conference.