Daily Pollution Research Articles

What are public preferences for air quality improvement policies? Additional information from extended choice models

Effectively assessing public preferences for air quality improvement policies is extremely important to environmental policy formulation, but developing policies that cater to public tastes is a great challenge. Although the random parameters logit (RPL) model in the choice experiment is widely used in relevant studies, it remains limited in revealing additional preference heterogeneity. Given this, the study applies two extended models in exploring public preference heterogeneity for air quality policies. An RPL model with heterogeneity in means and variances (RPL-HMV) and an RPL model with correlated random parameters (RPL-CRP) are used to provide more beneficial insights for policy analysis. The study shows that better-educated groups are more willing to pay for increasing urban green coverage, and income increases the randomness of such preferences’ distribution among groups. From the perspective of preferences, reducing heavy pollution days is positively associated with decreasing morbidity of respiratory diseases caused by outdoor air pollution and negatively correlated with improving urban green coverage. In addition, compared to the RPL-CRP model, the willingness to pay in the RPL model is overestimated by 14.72%. The study further clarifies public preferences for air quality policies, and the extra information revealed by extended models provides more valuable references for policy-making.

Quantified the influence of different synoptic weather patterns on the transport and local production processes of O3 events in Pearl River Delta, China

Regional ozone (O3) pollution in the Pearl River Delta (PRD) region has become a topic of discussion in recent years. The occurrence of regional O3 pollution are influenced by local emissions and cross-regional transportation. In this study, we identified the predominant synoptic patterns that were associated with regional O3 pollution from August to November in 2015–2021 using the Lamb-Jenkinson classification technique. All synoptic types were divided into four major categories of NE-type, C-type, S-type and A-type, which accounted for 42 %, 25 %, 18 % and 15 % of the total number of regional O3 pollution days, respectively. The weather conditions for each synoptic pattern were described by using MERRA-2 datasets. Then a rapidly method was established to quantify the contribution of cross-regional processes to high O3 concentration in different synoptic patterns over the PRD through the WRF-Flexpart model. The NE-type weather condition was characterized by a relatively large wind speed with a significant cross-regional transport contribution of 35.8 %. The A-type weather condition had moderate surface wind speed with the stable weather condition, resulting in a lower cross-region transport contribution of 27.7 %. Under controlled by C-type, the stagnant weather condition caused by low-pressure systems on its periphery, would suppress diffusion of O3. As a result, the regional O3 pollution in the PRD were mostly attributed to locally (87.9 %) with minimal cross-regional transport (12.1 %). The S-type weather condition was mainly associated with the West Pacific Subtropical High and the surface equalization pressure field, accompanied by low wind speed. Therefore, the considerable (minor) contribution of local production (cross-regional transport) of 83.3 % (16.7 %) to O3 pollution in the PRD is a consequence of the stagnation weather condition.

Prediction and Early Warning of Air Quality based on the LSTM-ARIMA Model

At present, China's economy is transforming from a stage of high-speed growth to a stage of high-quality development. Building an ecological civilization is an important part of the realization of the Chinese dream of national rejuvenation. Air pollution will cause harm to human health, ecological environment, social and economic aspects, and its pollution level is affected by many factors, such as PM2.5, PM10, CO, temperature, wind speed, precipitation and so on. In order to implement the party's 20th spirit, strengthen the coordinated control of pollutants, basically eliminate heavy pollution weather, improve and improve the response and disposal mechanism of heavy pollution weather, a place issued an emergency plan for pollution weather, which will strengthen monitoring and early warning, energy conservation and emission reduction, and minimize the impact of pollution weather. To explore the influencing factors of PM2.5 concentration change, more accurate prediction PM2.5 concentration and AQI index, this paper for the prediction and warning of air quality, using the daily pollutants in January 2015 to April 2023, the data of analysis of PM2.5 concentration, the importance of the top three factors PM10, average temperature, CO as auxiliary variables, to construct the LSTM-ARIMA combination model. First, LSTM was used for multi-factor prediction. In order to improve the accuracy of the prediction results, the prediction error of LSTM was then linearly corrected based on the ARIMA model, the time step was adjusted to observe the previous data, and the prediction results of the model were evaluated by root mean square error (RMSE).

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM2.5.

Rapid reduction of air pollution and short-term exposure risks in China

With the continuous control of anthropogenic emissions, China's air quality has improved significantly in recent years. Given this background, research on how the short-term exposure risks caused by air pollution in China have changed is insufficient. This study utilized hourly concentration data from ground observation stations and the official air quality guidelines of the Ministry of Ecology and Environment of China and the World Health Organization as standards to systematically investigate the spatiotemporal characteristics and short-term exposure risks of air pollution in China from 2015 to 2022. The results indicate that various atmospheric pollutants except for ozone showed a decreasing trend yearly. Nationwide, both single pollutant air pollution days (SAPDs) and multiple pollutant air pollution days (MAPDs) showed varying degrees of reduction within 15 and 25 days, respectively. SAPD was dominated mainly by excessive PM2.5 and PM10 pollutants, while MAPD was dominated mainly by excessive pollutant combinations, including PM2.5 + PM10, CO + PM2.5 + PM10, and SO2 + PM2.5 + PM10. As the concentration of atmospheric pollutants decreased, the total excess risk (ER) decreased yearly from 2015 to 2022, but there were significant regional differences. Now, the ER is less than 0.25% in southern China, in the range of 0.25%-0.5% in the North China Plain and some cities in the northeast, and higher than 1% in the northwest. Particulate matter is currently the primary pollutant posing short-term exposure risk in China, especially due to the impact of sandstorm weather. This study indicates that China's atmospheric cleaning action is significantly beneficial for reducing health risks.

Environmental pollutants increase the risks of acute exacerbation in patients with chronic airway disease.

Respiratory infections are a common cause of acute exacerbations in patients with chronic airway disease, however, environmental factors such as air pollution can also contribute to these exacerbations. The study aimed to determine the correlation between pollutant levels and exacerbation risks in areas exposed to environmental pollution sources. From 2015 to 2016, a total of 788 patients with chronic airway diseases were enrolled in a study. Their medical records, including hospital visits due to acute exacerbations of varying severity were analyzed. Additionally, data on daily pollutant levels from the Air Quality Monitoring Network from 2014 to 2016 was also collected and analyzed. Patients with chronic airway disease and poor lung function (FEV1 < 50% or obstructive ventilatory defect) have a higher risk of severe acute exacerbations and are more likely to experience more than two severe acute exacerbations within a year. The study found that in areas exposed to environmental pollution sources, there is a significant correlation between NO2, O3, and humidity with the main causes of severe acute exacerbation. When the levels of NO2 were higher than 16.65 ppb, O3 higher than 35.65 ppb, or humidity higher than 76.95%, the risk of severe acute exacerbation in patients with chronic airway disease increased. Acute exacerbations of chronic airway disease can be triggered by both the underlying disease state and the presence of air pollution. Computer simulations and early warning systems should be developed to predict acute exacerbations of chronic airway disease based on dynamic changes in air pollution.

Air pollution and topography in Tehran

Tehran is one of the most polluted cities in the world with 48 days of air pollution exceeding the admissible threshold (AQI &gt; 150) for 3 months of the 15 years studied. This period coincides with the time when Tehran’s inversion reaches its maximum stability. The purpose of this study was to determine the height of air pollution in Tehran in the days when pollution exceeds the permissible limit. Continuing to study the pressure and temperature conditions of these days, we then considered the geographical and topographic conditions, and finally identified the best of these cells for potential theoretical air turbulence. The results of this study, based on the Harmonic Analysis method and based on Tehran temperature and pressure data over a 15-year period (2003–2017), show that the highest elevation of Tehran inversion does not exceed 1800 m on polluted days. Only within 6 days of those beyond the admissible threshold, temperature and pressure cells with the highest Newtonian mass are formed. The center of these cells formed with a compressive difference of 32 mg in November, 7 mg in January, 11 mg in December, and temperature difference of 1.1° in November, 4.4° in January, and 1.9° in December. Generally, we considered the formed cells by the temperature and pressure difference and the gradient between them, as well as the difference in height between the cells and their location. This information, combined with the local winds causing the differences in temperature and pressure, allows us to elucidate conditions for creating air turbulence in Tehran and mitigating the amount and degree of air pollution.

Analysis of the Actual Usage and Emission Reduction Potential of Electric Heavy-Duty Trucks: A Case Study of a Steel Plant

In order to understand the driving characteristics of electric heavy-duty trucks in practical application scenarios and promote their usage to replace diesel trucks, this study analyzed the actual usage of electric and diesel heavy-duty trucks in a steel factory based on vehicle-monitoring data and remote online monitoring data and estimated the emission reduction potential of the application of electric trucks by using a mileage-based method and the greenhouse gas emission model. The results showed that the electric heavy-duty trucks in the steel factory mostly operated for over 14 h, with a vehicle kilometers traveled (VKT) of 50–300 km each day, which could meet most of the demands of the transportation of the steel industry. The average daily energy consumption for most trucks falls within the range of 210–230 kWh/100 km, with higher consumption in winter than in summer, which can save approximately 18–26% in operating costs compared with diesel trucks. It is estimated that the usage of these electric heavy-duty trucks can achieve an annual reduction of 115.8 tons of NOx emissions, 0.7 tons of PM emissions, and 18,000 tons of CO2 emissions. To further promote the application of electric heavy-duty trucks in China, several policy suggestions, such as introducing priority road-right policies, promoting vehicle and battery leasing markets, and exempting zero-emission vehicles during heavy pollution days, were proposed.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013–2020

BackgroundThe spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 μm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 μm), have not been well studied. ObjectiveTo quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS). MethodsAmbient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013–2020. ResultsThe average NYS resident was exposed to 4451 #·cm−3 UFP and 7.87 μg·m−3 PM2.5 in 2013–2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by −6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5–13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DiscussionWe identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP’s limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions.The ML models are ranked based on their accuracy, with the order being XGBoost > MLP >SVRrbf > RF > Bayesian >SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model’s outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days.

Missing data imputation strategies: A comparative analysis of hourly and daily pollution models

Epidemiological studies have consistently demonstrated the association between daily pollution exposure and disease outcomes. To estimate daily exposure, hourly pollution data are commonly aggregated, but missing data pose a significant challenge to this approach. To overcome this issue, some researchers have developed various models to impute missing hourly data. Alternatively, directly modelling pollution exposure on a daily basis is possible, thereby avoiding the computational burden of hourly pollution modelling. However, the performance of these two modelling strategies remains unclear. This study conducts a comparative assessment between hourly and daily modelling strategies for the purpose of estimating daily pollution exposure. Utilizing data derived from Guangzhou city, the analysis encompasses diverse scenarios of data absence. The outcomes consistently highlight the superior performance of daily pollution models in terms of mitigated bias and diminished root mean square error (RMSE) values.

Characterization of size-resolved effective density of atmospheric particles in an urban atmosphere in Southern China

Effective density (ρeff) is one of the most important physical properties of atmospheric particles, providing important references in exploring the emissions and aging processes of fresh particles. In this study, a combined system of differential mobility analyzer, centrifugal particle mass analyzer, and condensation particle counter was used to periodically measure the ρeff of atmospheric particles in Shenzhen from Oct. 2021 to Jan. 2022. Results showed that the ρeff of particles with various size presented a bimodal distribution, which could be divided into main density (ρm, main peak, corresponding to relatively dense particles after aging) and sub density (ρs, sub peak, corresponding to fresh particles). The occurrence frequencies of ρs of particles with diameters of 50 and 80 nm were less than 20%, but were as high as about 40% of that with diameters from 120 to 350 nm. The ρm showed increasing trend with the size of particles, while ρs decreased as the increasing of the size of particles. The ρeff on pollution day varied significantly with chemical compositions. The increasing of the proportion of sulfate could promote the increasing of ρeff, while black carbon and organic matter caused opposite effects, which may be related to various factors, including the difference of the material density and morphology of various chemical components. The ρeff of 50, 80 and 120 nm particles decreased considerably during the new particle formation event, indicating that organic condensation was an important contributor to new particle growth.

Correlation between air pollution and cognitive impairment among older individuals: empirical evidence from China

BackgroundLittle information is available regarding the impact of air pollution on cognitive impairment in older individuals in developing countries. This study empirically tested the impacts of the air quality index (AQI), air pollution intensity (quantified by the number of days of extreme air pollution in a year), and different pollutants on the cognitive abilities of older Chinese individuals.MethodsA panel of 28,395 participants spanning 122 cities in 2015 and 2018 was used, based on 3-year follow-up survey data from the China Health and Retirement Longitudinal Study (CHARLS) database. Data from the two phases of the CHARLS microsurvey were combined with relevant statistical data on air pollution in each region in the current year. These two surveys were used to investigate changes in basic health and macro-environmental indicators in older individuals in China, and a mean difference test was conducted. We then reduced the sample selection error by controlling for environmental migration and used two-way fixed and instrumental variable methods for endogenous treatment to avoid the estimation error caused by missing variables.ResultsAir pollution had a significantly negative effect on the cognitive abilities of older individuals (odds ratio [OR]: 1.4633; 95% confidence interval [95% CI]: 1.20899–1.77116). Different pollution intensities(only AQI value is greater than 200 or more) had apparent effects on cognitive impairment, with an OR of approximately 1.0. Sulfur dioxide had significantly negative effects on cognitive ability, with OR of 1.3802 (95% CI: 1.25779–1.51451). Furthermore, air pollution impact analysis showed heterogeneous results in terms of age, sex, education, and regional economic development level. In addition, social adaptability (calculated using social participation, learning, adaptability, and social support) not only had a significant positive effect on the cognitive abilities of older individuals, but also regulated the cognitive decline caused by air pollution.ConclusionsAir pollution affects cognitive impairment in older individuals, especially in those with lower education levels, and living in economically underdeveloped areas. This effect is synchronous and has a peak at an AQI of > 200.

Geochemical characteristics and socioeconomic associations of carbonaceous aerosols in coal-fueled cities with significant seasonal pollution pattern

Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), are critical component of fine particulate matter (PM2.5), with diverse impacts on air quality and human health. This study investigated the concentrations and seasonal patterns of carbonaceous species in PM2.5 during both the heating season (January 2021) and non-heating season (July 2021) in three coal-fueled cities in northern China, as well as the differences in carbonaceous aerosols and their associations with socioeconomic parameters in cities situated on either side of the “Hu Line” in China. The results showed that, owing to intensified coal combustion and unfavorable meteorological conditions, levels of OC, EC, and OC/EC ratios were higher in winter compared to summer. Moreover, the presence of dust (DU) and light pollution (LP) days resulted in elevated OC levels but decreased EC levels. The Char-EC/Soot-EC ratios were highest during LP, followed by CL and DU. A source apportionment analysis demonstrated that coal burning, vehicle exhaust, road dust, and biomass burning were the primary contributors to carbonaceous aerosols, as confirmed by diagnostic ratios, Char-EC/Soot-EC ratios, and PCA analysis. Furthermore, our study found that carbonaceous aerosols concentrations and source apportionment primarily varied with diurnal and seasonal trends and different pollution types. Additionally, at the national scale, population density and urban green space exhibited a positive correlation with OC/EC ratios (p < 0.05), while energy consumption per unit of GDP showed a negative correlation (p < 0.05). The observation that OC/EC ratios were lower in coal-fueled cities than in economy-based cities suggests a more severe pollution scenario. These findings highlight the importance of comprehending of the seasonal variation and chemical characteristics of carbonaceous aerosol for understanding air pollution sources and characteristics, which is essential for both air quality management and human health.

Practical application for legacy acid mine drainage (AMD) prevention and treatment technologies in karst-dominated regions: A case study

Acid mine drainage (AMD) from abandoned mines in karst-dominated regions in southwestern China was causing contamination of groundwater and surface streams. To avert the unwise decisions of “pollution first before treatment” during pre-mining, mid-mining and post-mining activities, this paper proposes a contaminant migration prevention technical framework covering 4 comprehensive processes. The formation mechanism of spring pollution, engineering remediation processes and contamination treatment effects were described in Longdong Spring. In 2018, the Longdong Spring water had Fe 33.83 mg/L and Mn 3.60 mg/L, exceeding the Chinese surface water standard (0.3 mg/L and 0.1 mg/L in GB 3838–2002) by 112 and 36 times, respectively. In 2020, after grout blocking, in situ treatment and wetland remediation, the highest Fe was 4.5 mg/L in a short period, and the spring water pollution days in this year were 42 days compared with the previous 320 spring water pollution days in 2018. In 2021, two years of remediation with the implementation of terminal remediation wetlands, the Fe was less than 0.03 mg/L compared with the previous 33.83 mg/L, and the water quality reached water standard (less than 0.3 mg/L). At present, Longdong Spring has become one of the most beautiful natural local landscapes.

Oxidation capacity changes in the atmosphere of large urban areas in Europe: Modelling and experimental campaigns in atmospheric simulation chambers

Air pollution is a major concern for human health and the environment. Consequently, environmental standards have become stricter to improve air quality. Thanks to this, the ambient levels of O3 precursors such as VOCs and NOX have decreased. However, O3 levels in Europe, especially during winter, have increased, potentially impacting on atmospheric oxidation capacity and the associated chemistry of tropospheric oxidants. In this work, we focus on recent changes in the oxidation capacity of urban atmospheres. The study is conducted with the results of the CMAQ modelling system with a regional resolution with 12 × 12 km2 across the entire European continent for the winter (January) and summer (July) of 2007 and 2015. The 2015 meteorological data is used for both years to emphasise emission changes during the studied period. We scrutinise the changes in ambient concentration levels of the main tropospheric oxidants (O3 and HOX radicals) in five representative cities, Valencia, Madrid, Milan, Berlin, and The Hague. The enhanced O3 formation in winter seems to be due to the low VOC/NOX ratio, while the opposite trend in summer may be related to a relatively high ratio. Additionally, photooxidation experiments are carried out in the EUPHORE chambers to study the effect of changes in NOX concentration and NO/NO2 ratio on the variation of the given oxidants at constant VOCs concentrations. For the baseline experiments, two scenarios are selected based on the model results of 2015: two representative winter and summer days of low and high pollution in Berlin and Madrid, respectively. The role of VOC/NOX and NO/NO2 ratios on atmospheric reactivity is discussed. As a result, it is first suggested that further decreases in ambient NOX levels are required to reduce ambient O3 levels. Moreover, additional factors should be considered when designing local-specific emission abatement strategies.

Pollution characteristics, sources, and photochemical roles of ambient carbonyl compounds in summer of Beijing, China

Ambient carbonyls are important precursors of radicals and ground-level ozone (O3). In this study, sources, precursors, and impacts on radicals and O3 of carbonyls were investigated based on online observations of volatile organic compounds (VOCs) at an urban site in Beijing during June 2021. Carbonyls accounted for 36% and 42% of mixing ratios and OH reactivity for total measured VOCs, respectively. Formaldehyde was the most abundant carbonyl, with the mean level of 4.13 ± 2.28 ppb. Source apportionment results based on the multi linear regression (MLR) method suggested that secondary production contributed 41%, 25%, 36%, and 30% of formaldehyde, acetaldehyde, propanal, and acetone, respectively. Key precursors of carbonyls were then identified based on the calculation of their production rates. It was found that alkenes contributed 59%–80% of aldehydes production. Impacts of carbonyls on HOx radicals (OH and HO2) and O3 production were explored using a box model based on observations (OBM). Photolysis of HONO, formaldehyde, and O3 were the dominant primary sources of HOx radicals during daytime of O3 pollution days, with average relative contributions of 52%, 28%, and 19% to the total primary production rate of HOx, respectively. Aldehydes accounted for 32% (20% from formaldehyde) of average HOx removal rates. The relative incremental reactivity (RIR) values of NOx determined by the OBM were negative, suggesting that the O3–VOCs-NOx sensitivity was in the VOCs-limited regime. Using the observed concentrations of carbonyls as constraints of OBM, the absolute values of RIR for NOx tended to increase but those for anthropogenic VOCs tended to decrease. Formaldehyde showed the largest RIR value for anthropogenic VOCs during O3 pollution days. These findings indicated the important impacts of carbonyls on O3 production and O3–VOCs-NOx sensitivity.

Simulation and control of non-point source pollution based on MIKE model: A case study of Danjiang river basin, China

In the world, non-point source (NPS) has become one of the important factors affecting the water environment and water ecological security in the basin. In this paper, we investigate the response of runoff to pollution loads by establishing the MIKE SHE and MIKE HYDRO River coupled model with the Danjiang river basin as the research object; The MIKE LOAD is used to analyze the spatial and temporal distribution characteristics of agricultural NPS pollution and to perform equivalence load analysis of pollutants in the basin; Single and combined control measures are set up to simulate the reduction effect of each pollutant for NPS pollution control in the basin. The results show that daily runoff has a high correlation with daily pollution loads of ammonia nitrogen (NH3-N) and total phosphorous (TP); The pollution load of Jingziguan section calculated by the coupled model is comparable with the results calculated by MIKE LOAD, and the relative error is below 30%, and the simulation results are reasonable. The trend of the load into the river for all pollutants is consistent with the rainfall and generally shows the characteristics of high loads in flood season and low loads in non-flood season. The total emissions of NPS pollutants in the basin are TN>NH3-N>TP>COD, and the loads share of the four pollutants are 52.82%, 21.31%, 19.42% and 6.44% respectively. Four single and one combination measures are set up to evaluate the pollutant reduction effects. The reduction effect of combined measures is significantly better than that of single measures.

Pollution hot spots and the impact of drive-through COVID-19 testing sites on urban air quality

To be successful, commitments to climate change and environmental policy will require critical changes in human behavior and one important example is driving and idling. Idling is defined as running a vehicle’s motor while not in motion. Idling studies have repeatedly demonstrated that this behavior is costly, harmful to human health, and highly polluting. However, with the onset of COVID-19, the use of drive-through services to provide food, pharmaceuticals, and medical testing has increased. To understand this phenomenon further, we worked cooperatively with our government partners to compare the concentrations of PM2.5 at three regulatory sensor locations with nearby drive-through COVID-19 testing sites during average to elevated pollution days. Salt Lake City, UT (USA), where this study was undertaken, has seen a dramatic rise in drive-through services since the onset of the pandemic and community concern is also high due to poor local air quality. More importantly, the Salt Lake Valley is home to one of the largest research grade air quality networks in the world. Fine particulate matter sensors were installed or already in place at or adjacent to COVID-19 testing sites in the area, and we used data from nearby Utah Division of Air Quality monitors to provide comparative PM2.5 concentrations. Due to their placement (e.g., further distance from large roads and other emitting sources), we found that testing sites showed lower PM2.5 concentrations during average air quality days despite increased idling rates. However, when urban pollution rates were elevated due to atmospheric inversions, extensive idling around testing sites led to hyper local PM2.5 concentrations or pollution hot spots. This suggests that idling has serious compounding effects in highly polluted urban areas and policies minimizing vehicle emissions from idling and congestion could conceivably curtail pollutant exposure in a range of settings.

Multi-objective optimal dispatch strategy for power systems with Spatio-temporal distribution of air pollutants

Accurate and effective pollutant emission control models are key in mitigating the environmental impact of energy supply systems. This paper proposes a novel high-dimensional multi-objective optimal dispatching strategy for power systems considering the spatial and temporal distribution of multiple pollutants. Firstly, a spatial and temporal distribution model of pollutants in thermal power plants is constructed, which is truly applicable to power dispatch. For modeling the pollution situation, the daily changes in the atmospheric boundary layer are taken into account, fully reflecting the pollutant dispersion characteristics of thermal power plants and improving the result accuracy. Next, a high-dimensional multi-objective optimization model is developed to simultaneously reduce the cost of power generation, carbon emission and the impact air quality from VOCs (volatile organic compounds), SO2 and NO2. This model combines the spatial and temporal distribution characteristics of various pollutants and environmental capacity. Finally, a representative high-dimensional multi-objective optimization algorithm is employed to obtain an approximate Pareto-optimal solution set. And a multi-objective decision method is proposed to filter the compromise solution. The results show that the APCs of VOCs, SO2 and NO2 are reduced by 27.5%, 10.13% and 23.16%, respectively, in the pollution day model. On non-pollution days, the proposed scheduling method not only effectively improves air quality, but also achieves cost savings of $1276.59 and reduces CO2 emission by 0.168 × 104t compared to the emission limitation method. The proposed scheduling method can not only effectively improve air quality, but also make corresponding adjustments according to the spatial and temporal changes of environmental capacity, which can truly realize economic and environmental-friendly power scheduling.