Impacts of transportation sector emissions on future U.S. air quality in a changing climate. Part I: Projected emissions, simulation design, and model evaluation

Assessing the impacts of climate variables on long-term air quality trends in Peninsular Malaysia

Climate change is thought to influence the composition of atmospheric air, but little is known about the direct relationship between these variables, especially in a hot tropical climate like that of Malaysia. This work summarizes and analyzes the climate state and air quality of Peninsular Malaysia based on selected ground-based observations of the temperature, precipitation, relative humidity, wind speed, wind direction and concentrations of PM10, O3, CO, NO2, and SO2 over the last 20 years (2000-2019). The relationship between the climate state and air quality is analyzed using the Pearson correlation and canonical correlation analysis (CCA) methods is employed to predict the degree of change in the future air quality under different warming scenarios. It is found that the Peninsular Malaysia mainly experienced strong precipitation in the central and mountainous regions, while air pollutants are primarily concentrated in densely populated areas. Throughout the period of study (interannual, monthly, and diurnal time series analyses), Peninsular Malaysia became warmer and drier, with a significant increase in temperature (+4.2 %), decrease in the relative humidity (-4.5 %), and greater fluctuation in precipitation amount. The pollution conditions have worsened; there has been an increase in the PM10 (+16.4 %), O3 (+39.5 %), and NO2 (+2.1 %) concentration over the last 20 years. However, the amount of SO2 (-53.6 %) and CO (-20.6 %) decreased significantly. The analysis of the monthly variation shows a strong bimodality of the PM10 and O3 concentrations that corresponds to the monsoon transition. Intensive diurnal fluctuations and correlations are observed for all the variables in this study. According to the CCA, the air quality factors are strongly correlated with meteorological factors; in particular, the CO, O3, and PM10 concentrations interact strongly with the air temperature. These findings show that the future air quality in Peninsular Malaysia has high possibility to deteriorate under warming condition.

Success factors for renewable energy businesses in emerging economies

Purpose

With a growing demand for safe, clean and affordable energy, emerging economies (EEs) across the globe are now seeking to create and rapidly develop renewable energy (RE) businesses. The success of these businesses often hinges on their ability to translate RE into sustainable value for energy consumers and the multiple stakeholders in this industry. Such value includes low production costs due to an abundance of natural resources (e.g. wind, water and sunlight) and public health benefits from reduced environmental pollution. With that in mind, this paper aims to gauge RE’s potential for sustainable value creation and then develop an effective RE business strategy.

Design/methodology/approach

This paper develops a structural equation model, conducts an exploratory factor analysis, confirmatory factor analyses with and without common latent factors and proposes a moderated mediation analysis to identify a host of factors that influence the success of RE businesses.

Findings

This paper discovers that RE business performance is significantly affected by integrated vision, intellectual capital and social capital.

Originality/value

To the best of the authors’ knowledge, this paper is one of the first empirical studies that identify various factors influencing successful RE businesses in EEs such as Asian (e.g. China and India) and Latin American countries.

Road traffic density and recurrent asthma emergency department visits among Medicaid enrollees in New York State 2005-2015

BACKGROUND

Environmental exposures such as traffic may contribute to asthma morbidity including recurrent emergency department (ED) visits. However, these associations are often confounded by socioeconomic status and health care access.

OBJECTIVE

This study aims to assess the association between traffic density and recurrence of asthma ED visits in the primarily low income Medicaid population in New York State (NYS) between 2005 and 2015.

METHODS

The primary outcome of interest was a recurrent asthma ED visit within 1-year of index visit. Traffic densities (weighted for truck traffic) were spatially linked based on home addresses. Bivariate and multivariate logistic regression analyses were conducted to identify factors predicting recurrent asthma ED visits.

RESULTS

In a multivariate model, Medicaid recipients living within 300-m of a high traffic density area were at a statistically significant risk of a recurrent asthma ED visit compared to those in a low traffic density area (OR = 1.31; 95% CI:1.24,1.38). Additionally, we evaluated effect measure modification for risk of recurrent asthma visits associated with traffic exposure by socio-demographic factors. The highest risk was found for those exposed to high traffic and being male (OR = 1.87; 95% CI:1.46,2.39), receiving cash assistance (OR = 2.11; 95% CI:1.65,2.72), receiving supplemental security income (OR = 2.21; 95% CI:1.66,2.96) and being in the 18.44 age group (OR = 1.59;95% CI 1.48,1.70) was associated with the highest risk of recurrent asthma ED visit. Black non-Hispanics (OR = 2.35; 95% CI:1.70,3.24), Hispanics (OR = 2.13; 95% CI:1.49,3.04) and those with race listed as "Other" (OR = 1.89 95% CI:1.13,3.16) in high traffic areas had higher risk of recurrent asthma ED visits as compared to White non-Hispanics in low traffic areas.

CONCLUSION

We observed significant persistent disparities in asthma morbidity related to traffic exposure and race/ethnicity in a low-income population. Our findings suggest that even within a primarily low-income study population, socioeconomic differences persist. These differences in susceptibility in the extremely low-income group may not be apparent in health studies that use Medicaid enrollment as a proxy for low SES.

Dynamics of radiative Williamson hybrid nanofluid with entropy generation: significance in solar aircraft

Sun based energy is the chief source of heat from the sun, and it utilizes in photovoltaic cells, sun-based power plates, photovoltaic lights and sun-based hybrid nanofluids. Specialists are currently exploring the utilization of nanotechnology and sun-based radiation to further develop flight effectiveness. In this analysis, a hybrid nanofluid is moving over an expandable sheet. Analysts are presently exploring the utilization of nanotechnology and sunlight-based radiation to further develop avionics productivity. To explore the heat transfer rate phenomenon, a hybrid nanofluid stream is moving towards a trough having a parabolic type shape and is located inside of solar airplane wings. The expression used to depict the heat transfer phenomenon was sun based thermal radiation. Heat transfer proficiency of airplane wings is evaluated with the inclusion of distinguished effects like viscous dissipation, slanted magnetic field and solar-based thermal radiations. The Williamson hybrid nanofluid past an expandable sheet was read up for entropy generation. The energy and momentum expressions were solved numerically with the utilization of the Keller box approach. The nano solid particles, which are comprised of copper (Cu) and Graphene oxide, are dispersed utilizing SA (Sodium alginate) as an ordinary liquid (GO). A huge number of control factors, for example, temperature, shear stress, velocity, frictional element along with Nusselt number are investigated in detail. Intensification of thermal conduction, viscous dissipation and radiation improve the performance of airplane wings subjected to heat transmission. Hybrid nanofluid performance is much better than the ordinary nanofluid when it comes to heat transmission analysis.

Framework for the Introduction of Vehicle-to-Grid Technology into the Polish Electricity Market

Vehicle-to-grid (V2G) technology is one of the advanced solutions that uses electric vehicles (EV) to balance electricity demand in the power system. It can be particularly useful in analyzing and then mitigating the risk of not delivering electricity to the end user. Therefore, it is necessary to analyze the possibility of operation of this technology in the legal framework. The article presents the analysis of the legal status in Poland, referring to the documents of the European Union and domestic legislation. Potential changes in Polish energy law that could facilitate the implementation of V2G technology are also proposed. In addition, the authors suggested the principles for the use of this technology, formulating a mechanism called the V2G Program. Within this Program, the V2G Service was defined and a business model of its implementation by a participant of the V2G Program (uEV) was presented. In addition, an uEV selection algorithm is provided so that the mathematical model of the V2G Service can be validated. Based on the performed simulations, it can be concluded that the implementation of the V2G Program requires significant changes in the Polish energy law, but it is feasible from the technical point of view.

Linking energy sector and air quality models through downscaling: Long-run siting of electricity generators to account for spatial variability and technological innovation

Modeling the air pollution implications of long-term energy transitions requires a downscaling process as an intermediate step between national-scale energy models and fine-scaled air quality models. Traditional "Grow-in-Place" (GIP) downscaling methods assume that future patterns of generator siting and emissions will be similar to those in the past. However, rapid technological change and shifting policy might yield very different future spatial patterns of power emissions. Here, we propose a "Site-and-Grow" (SAG) downscaling framework to couple the Electricity Market Module (EMM) of the National Energy Modeling System (NEMS) with the Community Multi-scale Air Quality (CMAQ) model to simulate future changes in emissions from power sector. The SAG framework consists of two steps. First, we downscale regional energy information to subregions using a modified generation expansion model under the assumption that economic fundamentals drive decisions at that scale. Second, we use GIS-based screening to locate potential sites for new power plants, and specify the final county-level placement using a multicriteria value function, assuming that land use and environmental constraints are most influential. The method is implemented in one EMM region (Carolinas and Virginia) as a case study. We compare spatial and temporal variability of downscaled emissions using both GIP and SAG methods, as well as emissions differences among four NEMS scenarios (base case, high natural gas consumption, high penetration of electric vehicles, and marine vessel electrification in ports). The results indicate that coal power plant emissions such as SO₂ and NO_x continue to dominate emissions from all other traditional power plants even in 2040, which suggests that emission changes will mainly be determined by where old coal plants are retired. An ANOVA (analysis of variance) comparison of four energy scenarios with two downscaling methods shows that the choice of downscaling method can contribute as much to emissions patterns as much as the choice of scenario.

Alternative fuel technologies emissions for road heavy-duty trucks: a review

Many alternative fuel technologies have been studied for the transport sector to increase its sustainability while reducing costs, greenhouse gases (GHG), and air pollution emissions. Nevertheless, conventional diesel is still the predominant fuel for heavy-duty trucks. Road freight transport consumes 25% of the world's energy and is responsible for emissions with local health impacts and the global greenhouse effect. In this context, this paper reviewed items from 2015 to 2020 to analyze the technologies available for the road freight transport regarding pollutant and GHG emissions. Results are presented in two parts: first quantitatively, quantitative data was extracted from reviewed papers and statistically treated and, second, qualitatively through a comparative chart, which shows the impact on air pollutants from the use of a different type of fuels. In general, papers are mostly concerned with particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HC) emissions due to its impact on public health, with a low number of papers covering GHG emissions. The trade-off between different fuels and how this process can impact emissions, sometimes increasing or decreasing specific pollutants, is discussed. According to the analyzed papers, the main characteristics that affect the pollutant emissions are, in general, the fuel oxygen content and the combustion chamber temperature.

Development of a high-resolution emission inventory of agricultural machinery with a novel methodology: A case study for Yangtze River Delta region

Recent increased use of agricultural machines elevated the atmospheric pollutant emissions in the Yangtze River Delta (YRD) region in eastern China. Given the potentially large environmental and health impacts in busy seasons with enhanced machinery usage, it is important to accurately estimate the magnitude, spatial and temporal distributions of the emissions. We developed a novel method to estimate the real-world in-use agricultural machinery emissions, by combining satellite data, land and soil information, and in-house investigation. The machinery usage was determined based on the spatial distribution, growing and rotation pattern of the crops. The varied requirement of machinery power by heterogeneous soil texture, which was ignored in the previous studies, was considered in our methodology. The spatiotemporal pattern of machinery usage was determined based on the explored quantitative correlation between the local agricultural activity duration and the geographic location of the activity. A "grid-based" (30 × 30 m) inventory with daily emissions was then obtained, achieving significant improvement on spatial and temporal resolution. It substantially diminished the bias of previous inventories based on the machinery population or power installation census data. The emissions of NO_X, PM_2.5, CO and THC were estimated at 36300, 2000, 36900 and 8430 metric tons in YRD, with the majority contribution from Anhui and Jiangsu. Ten cities locating in northern and central Anhui and Jiangsu contributed the largest machinery emissions, accounting for 60% of the total emissions in YRD. Harvesting was found to have the largest emissions, followed by tilling and planting. Regarding the crops, the emissions from wheat and rice related machinery usage were the largest. In the busy seasons (spring and autumn), larger daily NO_X and PM_2.5 emissions were found from machinery than on-road vehicles in 42% of counties in Anhui and Jiangsu, highlighting the necessity of careful strategy making on controls of priority emission source.

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM₁₀ and PM_2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM_2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM_2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM_2.5 among 'children' age group than for PM₁₀ (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM_2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

Detection of PM2.5 plume movement from IoT ground level monitoring data

In this study, we analysed a data set from 10 low-cost PM_2.5 sensors using the Internet of Things (IoT) for air quality monitoring in Mae Sot, which is one of the most vulnerable areas for high PM_2.5 concentration in Thailand, during the 2018 burning season. Our objectives were to understand the nature of the plume movement and to investigate possibilities of adopting IoT sensors for near real-time forecasting of PM_2.5 concentrations. Sensor data including PM_2.5 and meteorological parameters (wind speed and direction) were collected online every 2 min where data were grouped into four zones and averaged every 15 min interval. Results of diurnal profile plot revealed that PM_2.5 concentrations were high around early to late morning (3:00-9:00) and gradually reduced till the rest of the day. During the biomass burning period, maximum daily average concentration recorded by the sensors was 280 μg/m³ at Thai Samakkhi while the minimum was 13 μg/m³ at Mae Sot. Lag time concentrations, attributed by biomass burning (hotspots), significantly influenced the formation of PM_2.5 while the disappearance of PM_2.5 was found to be influenced by moderate wind speed. The PM_2.5 concentrations of the next 15 min at the downwind zone (MG) were predicted using lag time concentrations with different wind categories. The next 15 min predictions of PM_2.5 at MG were found to be mainly influenced by its lag time concentrations (MG_Lag); with higher wind speed, however, the lag time concentrations from the upwind zones (MS_Lag and TS_Lag) started to show more influence. From this study, we have found that low-cost IoT sensors provide not only real-time monitoring information but also demonstrate great potential as an effective tool to understand the PM_2.5 plume movement with temporal variation and geo-specific location.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.