Sensitivity analysis to precursor emissions of multi-objective air quality control policies

A multi-objective optimization-based computational approach to nondestructive evaluation of damage in structural components, and more generally in solid continua, is discussed and numerically evaluated. The multi-objective approach provides a substantial improvement in the capabilities to traverse the optimization search space to minimize the measurement error and produce accurate damage estimates. Through simulated test problems based on the characterization of damage in structural steel components, including internal pipe surface geometry as well as material loss within a plate structure utilizing steady-state dynamic measurements of outer surface displacement, a multi-objective genetic algorithm optimization approach is shown to provide substantial computational improvement over single-objective strategies. The multi-objective approach consistently and efficiently produces more accurate characterization results in contrast to equivalent single-objective strategies. More importantly, the multi-objective approach is shown to exhibit consistently better tolerance to test measurement noise and measurement sparsity. Moreover, the multi-objective strategy was found to provide improved diversity in the solution estimates for ill-posed problems, which is an important step leading to insight into the necessary changes to the testing or parameterization to subsequently produce more accurate and unique solutions to such inverse characterization problems.

The determination of the optimal feed profiles for a reactive crystallizer is an important dynamic optimization problem, as the feed profiles offer a significant control over the quality of the product crystals. Crystallization processes typically have multiple performance objectives and optimization using different objective functions leads to significantly different optimal operating conditions. Therefore, a multiobjective approach is more appropriate for optimization of these processes. The potential for multiobjective optimization approach is demonstrated for semibatch reactive crystallization processes. The multiobjective approach usually gives rise to a set of optimal solutions, largely known as Pareto‐optimal solutions. The Pareto‐optimal solutions can help the designer visualize the trade‐offs between different objectives, and select an appropriate operating condition for the process. A well known multiobjective evolutionary algorithm, the elitist nondominated sorting genetic algorithm, has been adapted to illustrate the potential for the multiobjective optimization approach. © 2007 American Institute of Chemical Engineers AIChE J, 2007

The use of modeling to support environmental authorities to plan air quality control policies is now quite widespread in different parts of Europe. At the sub-national level the most common way to face the problem is through the scenario analysis approach, using Chemical Transport Models to assess the impact of emission reductions on pollution concentrations. In this paper a multi-objective approach to define air quality policies is proposed. The considered objective function is a vector including an Air Quality Index and an Internal Costs Index. In this work the Air Quality Index (AQI) is the yearly mean PM10 concentration over the study domain. It is estimated processing source-receptor models linking emission and concentrations over the domain cells, taking into account nonlinear phenomena and using Artificial Neural Networks (ANNs). The ExternE methodology is then applied to estimate health impacts and external costs of optimal control policies up to 2020. The data used to identify the source-receptor models has been provided by a set of 10 simulations computed through the TCAM (Transport Chemical Aerosol) model. The Internal Cost Index (CI) describes the costs to implement a particular emission reduction policy. This index is computed by means of ANNs linking emission reductions and their relative implementation costs, for different CORINAIR macro sectors. Simulations with IIASA’s GAINS model have been used to calibrate the emission-to-cost model. The solutions of the decision problem represent cost-effective policies at the sectoral level. The methodology is applied to Northern Italy, one of the most polluted areas in Europe, to select optimal control policies up to 2020.KeywordsIntegrated assessment modelsMulti-objective problem

Background: Air pollution has been linked to increased rates of mortality, but little is known about individual characteristics related to the increase in risk. Aims: To examine short term effects...

To control tropospheric ozone Environmental Authorities need to develop air quality plans, implementing emission reduction control strategies. This is a challenging task, mainly due to nonlinear processes that bring to ozone formation and accumulation, forced by precursor emissions and solar radiation. This is even more difficult when considering, at the same time, effectiveness and costs of alternative environmental policies. In this study a multi-objective mathematical problem is proposed, considering air quality and cost objectives. Air quality objective is formalized as source-receptor model identified processing simulations of a complex photochemical modelling system. Cost objective is formalized identifying cost functions linking technology cost to emission reduction. The proposed methodology has been applied to a complex domain in Northern Italy, including Milan metropolitan area, a region characterized by high ozone levels during summer season.

The level of air quality at public transport stations: The Case of Torhailoch-Ayat main road in Addis Ababa

Background/Objectives: The prime objective of this work is to study the condition and quality of the air in Coimbatore city by measuring Air Quality Index (AQI) and to compare the measured values with standard values. This work also examines the variation of ambient air quality with various climatic conditions. Methods/Statistical Analysis: The quality of air was determined based on National Ambient Air Quality Standards (NAAQS). This was carried out based on measuring four major air pollutants such as PM10, PM2.5, SO2 and NO2 at different climate conditions during January 2015 to December 2015. Respirable Dust Sampler (RDS) and Fine Particulate Sampler were used to determine PM10 and PM2.5 respectively. Findings: The present work shows its significance because there is no such data available for researchers for reference. Industrial (foundry units) and automobile emissions are the major pollution source which determines the quality of ambient air in the city. Among the different climatic conditions, summer exhibits more pollution level in the air while monsoon exhibits less pollution level. Results also reveals that the Industrial area experiencing more pollutants followed by commercial and residential areas. As per the AQI category, all locations are coming under moderate category except industrial location which requires stringent control measures to reduce the particulate matters in the air. Applications: Since no data is available for the Air Quality Index study of the region, this can be taken as a reference for future study.

The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.

Background: Emerging evidence suggests that air pollution is a significant but under recognized contributor to cardiovascular disease (CVD) mortality, even in middle age adults. This study evaluates the association between long term statewise air quality, as measured by median Air Quality Index (AQI) and cardiovascular mortality rates among adults aged 35–44 years across the United States over a 25-year period. Methods: We linked annual statewise median AQI data from EPA AQS (2000–2024) with CDC WONDER mortality records for non-infectious CVD in the 35–44 age group. States were ranked by mean AQI to define “Highest 10” and “Lowest 10” AQI cohorts. Mean annual cardiovascular crude death rates were compared between these groups using t-tests, and trends were visualized year-by-year. Correlation and linear regression analyses assessed the association between AQI and mortality across all state years as well. Results: States with the highest long-term AQI (poor air quality) had significantly higher mean cardiovascular mortality rates (mean 46.99 per 100,000) than states with the lowest AQI (good air quality) (mean 30.35 per 100,000; p = 0.01). Yearwise analysis showed a persistent gap in mortality rates with the highest AQI states consistently exhibiting greater CVD mortality across the study period. Linear regression confirmed a significant positive association between AQI and mortality (slope = 0.62, p < 0.001), although AQI alone explained only a modest portion of the variation (R 2 = 0.095). Conclusions: Higher long term air pollution exposure, as measured by AQI, is associated with increased cardiovascular mortality among U.S. adults aged 35–44 years. This relationship is robust at the extremes of air quality and persists over time. This highlights the need for public health policies to improve air quality as a means to reduce early onset CVD mortality. Further studies should explore mechanisms and policy interventions to mitigate this risk.

Optimization of transverse shear moduli for composite honeycomb cores

Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm

IntroductionNigeria is committed to reducing industrial trans-fatty acids (iTFA) from the food supply, but the potential health gains, costs and cost-effectiveness are unknown.MethodsThe effect on ischaemic heart disease (IHD) burden,...

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteor-ology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.

Multi-Objective Optimization Approach for Improving Performance of Building Energy efficiency measures are different from energy efficiency and cost effectiveness perspective. For decision maker it is hard to make right decision about different energy efficiency measure combinations in building. It is a complex problem to choose the best energy efficiency measure combination as decision involves many different factors that should be taken in account. Decision on implementation of energy efficiency measure implementation usually depends on investment costs and pay back time. Standards like Latvian Building Code LBN 002-01 can't be used to achieve reasonable expenses in renovation of buildings. Therefore, in order to find the optimal energy-efficiency measures, it is necessary to carry out optimization taking all the variable parameters into account. In the paper target function was presented that gives ability of the multi-objective optimization approach to handle the problem of improving energy efficiency in buildings. Case study is used to demonstrate the feasibility of the approach. 104. series soviet type dwellings was analysed to optimized insulation thickness for external walls. Even if accord with the LBN 002-01 it is enough to use 7 cm thick isolation (λ-0,039 W/(m2K)) layers optimal insulation layer is 12 cm (λ-0,039 W/(m2K)).

Brain Fog: Does Air Pollution Make Us Less Productive?