Weather Pollution Research Articles

Effects of Meteorological Parameters on Gaseous Air Pollutant Concentrations in Urban Area of Gwalior City, India

This study offers a complete urban air assessment taking into account the weather conditions and pollution sources. An assessment of meteorological conditions affecting the air quality and correlation among concentrations of pollutants and meteorological factors was done. The correlation between temperature, relative humidity, wind speed and sulfur dioxide and nitrogen dioxide concentrations was studied separately from the analysis of the monthly concentrations of these gaseous pollutants. Four urban areas of Gwalior were selected for this study to represent the city. For sampling and assessment, the guidelines of central pollution control board India were followed.

Effect Simulation of Responding Measures During a Heavy Pollution Red Alert in Tianjin City

The WRF/SMOKE/CMAQ model is used to simulate a heavy pollution process in the winter of 2016 in Tianjin. Three types of simulation scenarios are set up for the activation of red alert due to the “Tianjin Heavy Pollution Weather Emergency Plan”. The extinction effect of emergency measures on PM2.5 concentration is assessed quantitatively. Simulation results show that during this red alert period, a 30% reduction of industrial production can be implemented to reduce the average PM2.5 concentration by 3% to 10%, while odd-and-even license plate rule of motor vehicle can be implemented to reduce the average PM2.5 concentration in the city by 3% to 5.5%.The simultaneous implementation of two measures can reduce the average PM2.5 concentration by 5% to 12%.From the perspective of PM2.5 peak and mean drop under the three control scenarios, the control effect of industrial limited production measures on the peak value is more obvious than the mean value. The control effect of the vehicle’s line-limit measures on the mean is more obvious than the peak value. The control effect of the peak value and mean value control of PM2.5 under industrial and motor vehicle simultaneous measure scenarios is similar to that of industrial limited production measures. It shows that industrial emissions are still the main source of contribution during heavy pollution, and that vehicle control has played a certain role.

The Analysis of the Weather Conditions of a High Visibility Heavy Pollution Event in Shenyang

There was a high visibility heavy pollution event in Shenyang on October 21st, 2014. This paper analyzes the formation and maintenance of the heavy pollution weather from the aspects of weather situation, meteorological elements, atmospheric stability and pollutants transportation. The results show that the heavy pollution weather process is due to the surface layer atmosphere transports local atmospheric pollutants into the air by turbulent motion, and the inversion of the upper block pollutants diffuse into the sky, plus the northeaster wind transport, the heavy pollution occurs in Shenyang area. In the event of heavy pollution, the relative humidity is relatively low, and the moisture absorption effect of particulate matter in the atmosphere is limited, so the atmospheric visibility is not reduced to a lower level.

Refine Health Benefit Prior-Evaluation of Emergency Response Measures Under Heavy Pollution Weather

In a realistic scenario people move and get exposed to different microenvironment (ME) with varying PM2.5 levels. While the traditional health impacts assessment directly overlaid static population with ambient PM2.5 concentrations, without considering diverse individual Activity Patterns (AP), bringing great uncertainties in PM2.5-related mortality burden assessment.So to refine exposure assessment considering AP, and applied it to the assessment of the Emergency Response Measures (ERMs) for heavy pollution weather. We carried out Activity Pattern Survey (APS) to investigate the time spent in certain ME and on certain activity types. Based on population density maps and APS data, the Random Forest Model and Agent Based Model were used to establish the rules of individual activity decisions and simulate the Population Distribution Embedded with AP (PD-AP), respectively. Then, indoor/outdoor PM2.5 concentration ratios of ME were summarized through literature research and were applied to calculate the ME PM2.5 levels by modifying the ambient PM2.5 levels. Finally, based on PD-AP, ME PM2.5 levels, and doses-response relationship, we used the Relative Risk Model to evaluate PM2.5 attributed deaths under 8 ERMs. And we also compared results under PD-AP, Uniform Population Distribution (UPD) and Static Population Distribution (SPD), to figure out the importance of improving simulation ability of the real distribution of population.The traditional UDP and SDP methods overestimated PM2.5 attributed deaths, and this overestimation was far greater than changes of attributed deaths caused by choosing different ERMs. Therefore, it is of great importance of considering diversities of population AP for improving accuracy of PM2.5 exposures assessment. Among 8 ERMs, Intensified Control Scenario in Jiangsu should be selected to deal with heavy pollution weather for its maneuverability and good performance of reducing attributed deaths especially in highly polluted areas.

Understanding how changing rainfall may impact on urban drainage systems; lessons from projects in the UK and USA

Abstract Urban flooding and wet weather pollution are recognised as significant problems across the world, and changes in rainfall patterns arising as a consequence of climate change are likely to exacerbate these problems. This paper shares learning from a ground-breaking project led by CH2M for UK Water Industry Research and approaches used in other CH2M projects around the world. The UK project has explored the use of very high resolution (1.5 km) climate model output and climate analogues; other projects have used other methods to derive new design rainfall statistics commonly used in modelling wet weather collection systems for flooding and pollution investigations. Estimates of rainfall change have been used within collection system models to estimate the flooding and pollution impact of these changes. The methods applied in these projects can be replicated globally.

Calculation Of Ion Flow Field In HVDC Transmission Lines Under The Influence Of Haze

Because of the influence of haze on the electromagnetic environment of HVDC transmission lines, this paper proposes a method for calculating the ion flow field considering the influence of haze based on the flux line method, and takes the Yunguang 800kV UHVDC transmission line as an example. A synthetic field intensity and ion flow calculation model with different haze and haze levels was established, and the resultant field strength and ion flux density under different haze conditions were obtained. The results showed that the ground synthetic field intensity and ion flux density under haze weather increased relative to normal weather, and increased with the increasing degree of haze weather pollution. This calculation method has reference value for the analysis of haze weather and corona impact factors in the planning and design of HVDC transmission lines.

Modeling and predict environmental indicators for land leveling using adaptive neuro-fuzzy inference system (ANFIS), and regression

PurposeThe purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.Design/methodology/approachThis paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).FindingsBy applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R2 value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R2 with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.Originality/valueAs land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

Visibility dehazing based on channel-weighted analysis and illumination tuning

The air pollution and foggy weather often result in serious distortion while taking photos or recognizing patterns. He et al. have introduced the dark channel prior to solve this dehazing problem. Unfortunately, it cannot function well once the color difference of target image is large. More precisely, the dehazed result looks unnatural. Thus, we aim to develop a brand-new visibility dehazing technique based on the channel-weighted analysis and illumination tuning. The channel-weighted analysis is adopted to eliminate the unnatural effect, while the illumination tuning is applied to refine the details. Simulation results have demonstrated that the new method can guarantee the readability of a hazed image after removing noise, including the foggy photo and sandstorm one.

Construction and Application of Vertical Diffusion Index for Analyzing Weather During Pollution Events in Tianjin

Based on temperature and wind speed data from the 255 meter tall meteorological tower, the characteristics of atmospheric stability were analyzed in Tianjin, with the vertical diffusion index β and φ constructed by atmospheric chemical models. This provided information to use the vertical dispersion analysis method to forecast pollution from weather data. The results show that the comprehensive use of atmospheric stability and the vertical diffusion index can provide a better pollution forecast. When the atmospheric stability was D from 07:00-08:00 and 18:00-20:00, compared to when atmospheric stability was C, the probability of heavy pollution weather increased by 10 times. If the vertical diffusion index β and wind speed index were used to forecast heavy pollution, the accuracy rate was 67% higher than when using the single wind speed index. The coefficient between vertical diffusion index φ and PM2.5 mass concentration was 0.8.When the vertical diffusion index φ was less than 0.52, the probability of heavy pollution was 75%, identifying 59% of heavy pollution events.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

The Movement Characteristics of Charged Haze Particles in Ionized Field and Its Influence on Contamination of Insulator

The charged haze particles will affect the electric field of a high-voltage direct current (HVDC) transmission line and insulator contamination, and even result in pollution flashover. Based on the meshless local Petrov–Galerkin method, the influence of ionized electric field on haze particles’ motion is investigated using numerical calculation and experiments. Example of a coaxial electrode model is studied to verify the validation of the proposed method at first. It is proven that the proposed method agrees well with the measurements. Then, the motion principle of haze particles around an actual HVDC insulator is calculated under different ionized field levels. The result shows that the path of the charged haze particles will deflect along the electric field line driven by the electric field force, accelerating the precipitation of the haze particles on the dc insulators. Also, the contamination accumulation gets worse under severe pollution weather and high-voltage side of insulator.

The characteristics of atmospheric phthalates in Shanghai: A haze case study and human exposure assessment

While phthalates in indoor environments are extensively studied, reports on phthalates in outdoor air, particularly their associations with haze weather events are rare. Phthalates, especially dimethyl phthalate, are known to react with criteria air pollutants contributing to the formation of secondary organic aerosols. This study investigated phthalates levels in the atmosphere in Shanghai with a focus on their associations with different air quality weather events. The air quality during the study period was classified into three levels: non-haze, light pollution and moderate pollution. Phthalates levels were found to be lower in non-haze weather events (236 ng/m3) and higher in moderate pollution weather events (up to 700 ng/m3). Meteorological factors of relative humidity and wind speed had an inverse relationship with phthalates levels. Particulate matter had a positive correlation with phthalates levels. Hydroxyl radical initiated photo-reaction of dimethyl phthalate was evident by its inverse relationship with total atmospheric oxidant (O3 + NO2), indicating that dimethyl phthalate could be one of the precursors of secondary organic aerosol causing haze weather events. Daily intake of phthalates through exposure to outdoor air is estimated to be relatively minor; children intake remains higher on a body weight basis. This is the first study demonstrating the relationship of phthalates and different air quality conditions in haze weather events. The knowledge contributes to our understanding on the cause of haze weather events in China and elsewhere.

Construction of Fine Particles Source Spectrum Bank in Typical Region and Empirical Research of Matching Diagnosis

The research object of this paper is fine particles in typical region. The construction of component spectrum bank is based on the technology of online source apportionment, then the result of the apportionment is utilized to verify the effectiveness of fine particles component spectrum bank and which also act as the matching basis of online source apportionment receptor sample. On the next, the particle source of air pollution is carried through the matching diagnosis empirical research by utilizing online source apportionment technology, to provide technical support for the cause analysis and treatment of heavy pollution weather.

Evolutionary Design of a Carbon Dioxide Emission Prediction Model using Genetic Programming

Weather pollution is considered as one of the most important, dangerous problem that affects our life and the society security from the different sides. The global warming problem affecting the atmosphere is related to the carbon dioxide emission (CO2) from the different fossil fuels along with temperature. In this paper, this phenomenon is studied to find a solution for preventing and reducing the poison CO2 gas emerged from affecting the society and reducing the smoke pollution. The developed model consists of four input attributes: the global oil, natural gas, coal, and primary energy consumption and one output the CO2 gas. The stochastic search algorithm Genetic Programming (GP) was used as an effective and robust tool in building the forecasting model. The model data for both training and testing cases were taken from the years of 1982 to 2000 and 2003 to 2010, respectively. According to the results obtained from the different evaluation criteria, it is nearly obvious that the performance of the GP in carbon gas emission estimation was very good and efficient in solving and dealing with the climate pollution problems.

New climate change rainfall estimates for sustainable drainage

Sewer flooding, urban surface water flooding and wet weather pollution are recognised as significant problems in the UK and elsewhere in the world, and changes in extreme rainfall arising as a consequence of climate change are likely to exacerbate these problems. This paper reports on new approaches to estimate potential future rainfall intensity changes over the UK at temporal and spatial scales that impact on urban drainage systems. The research (part of UK Water Industry Research’s programme of climate change related projects) has produced estimates of rainfall intensity change over different parts of the UK using a climate analogue approach and a very high-resolution (1·5 km) climate model developed by the UK Met Office. The new estimates produced are, in general, higher than existing UK climate change allowances for rainfall intensity. Sewer flooding volumes, flooding frequency and frequency of pollution events are also investigated for one location; indicating that these incidents and flood volumes are also likely to increase in the future.

Observations of aerosol color ratio and depolarization ratio over Wuhan

The aerosol color ratio, depolarization ratio and aerosol optical depth (AOD) were measured by a two-wavelength-depolarization lidar at Wuhan, China during the period from May 2015–July 2016. The annual average AOD at Wuhan was about 0.33 during the period 2015–2016. The seasonal average AOD is small (0.26 ± 0.25) during the winter (December–February) season and large (0.4 ± 0.1) during the summer (June–August) season. The monthly average color ratio is small (0.23 ± 0.09) in January and large (0.76 ± 0.21) in August with an annual average value 0.54. The maximum monthly mean depolarization ratio (0.2 ± 0.07) occurred in the month of October, while the minimum (0.06 ± 0.02) occurred in the month of September, and the annual mean depolarization ratio was about 0.17. An analysis of temporal variations of color ratio and depolarization ratio suggests the presence of coarse and non-spherical particles during the autumn. The aerosol color ratio between 0.3 and 2.0 km was large (0.65), suggesting a large number of coarse particles in this range. The vertical distribution of the depolarization ratio is uniform. Finally, the spatial aerosol distribution under different weather conditions and its relationship with the color ratio is investigated in detail. The color ratio value of 0.74 could be used as a threshold for distinguishing polluted weather from clean weather. The aerosol optical and physical properties are investigated to provide a comprehensive understanding of aerosol radiative forcing and environmental problems in this region.

Observations of atmospheric pollutants at Lhasa during 2014–2015: Pollution status and the influence of meteorological factors

Atmospheric pollutants including SO2, NO2, CO, O3 and inhalable particulate matter (PM2.5 and PM10) were monitored continuously from March 2014 to February 2015 to investigate characteristics of air pollution at Lhasa, Tibetan Plateau. Species exhibited similar seasonal variations except O3, with the peaks in winter but low valleys in summer. The maximum O3 concentration was observed in spring, followed by summer, autumn, and winter. The positive correlation between O3 and PM10 in spring indicated similar sources of them, and was assumed to be turbulent transport. Temperature was the dominant meteorological factor for most species in spring. High temperature accelerates O3 photochemistry, and favors air disturbance which is conductive to dust resuspension in spring. Relative humidity (RH) and atmospheric pressure were the main meteorological factors in summer. RH showed negative correlations with species, while atmospheric pressure posed opposite situation. Wind speed (WS) was the dominant meteorological factor in autumn, the negative correlations between WS and species indicated diffusion by wind. Most species showed non-significant correlations with meteorological factors in winter, indicating the dependence of pollution on source emission rather than restriction by meteorology. Pollution weather character indicated that emissions were from biomass burning and dust suspension, and meteorological factors also played an important role. Air stream injection from the stratosphere was observed during O3 pollution period. Air parcels from Southwest Asia were observed during air pollution period in winter. An enhancement in air pollutants such as O3 would be expected in the future, more attention should be given to countermeasures for prevention of air pollution in the future.

Analysis of the Characteristics of the Haze in Recent 3 Decades in Chengdu

It is valuable to study the characteristics of haze change to make good efforts to control air pollution. The haze days in recent 3 decades in Chengdu were counted by 14 hour method, coming to the following results: 1) the number of haze days in Chengdu has decreased and then increased in the recent 3 decades; 2) the number of haze days in autumn and winter accounts for 70% of the year-round number. The spatial distributed characteristic of haze in Chengdu is that it is rare in northwestern Chengdu and common in southwestern Chengdu. Compared with Dujiangyan, the environmental background field of Chengdu, haze trends to become more serious in Shuangliu; 3) according to the comparative analysis on the AQI of 2013 and number of haze days, the methodologically-counted haze days correspond to moderate pollution weather, and this is well reflected in autumn and winter. It is susceptible to the rainfall system in summer; 4) the primary pollutant in Chengdu is PM2.5, followed by PM10. The climatic background is inimical to pollutant diffusion. Besides, the expansion of urban area and the change of people’s lifestyle are significant causes of the increase in the number of pollution days.

Study on the Correlation between Temperature and Chronic Obstructive Pulmonary Disease in Shijiazhuang, China—Retrospective Cohort

Using the chronic obstructive pulmonary disease (COPD) medical records from January 1st to December 31st of 2013 and the Meteorological observation data, the air pollution data in the same time periods, generalized additive models were used to quantitatively analyze the relationship between COPD hospitalizations and temperature with controlling the confounding effects of time trend, meteorological factors and air pollution index (AQI). Results showed: variable temperature in 24 h (BT), 3d lagged minimum temperature (Tm3) and 5d lagged diurnal maximum temperature and minimum temperature range (Tc5) have different effects on COPD hospitalizations. When BT is between -4.4°C and -0.7°C, the relative risk (RR) increases to 1.0207 (95% CI: 1.0074 - 1.0342）with every 1°C increase in BT; when Tm3 is between -3.6°C and 3.2°C, the relative risk (RR) increases to 1.0118 (95% CI: 1.0015 - 1.0222）with every 1°C increase in Tm3, and when Tm3 is greater than 20.5°C, the relative risk (RR) increases to 1.0069 (95% CI: 1.0005 - 1.0133) with every 1°C increase in Tm3; when Tc5 is between 0.9°C and 8.6°C, if the Tc5 increases 1°C, the relative risk (RR) increases to 1.0125 (95% CI: 1.0066 - 1.0185. There are different effects for weather in different seasons on COPD hospitalizations: in autumn and winter, it is mainly of little BT and heavy air pollution weather; in spring, the large Tc5 weather is a main feature, and in summer, it’s mainly of high temperature and low pressure weather. The results help to provide some guidance on COPD forecasting services.

UV Disinfection of Wastewater and Combined Sewer Overflows.

Municipal wastewater contains bacteria, viruses, and other pathogens that adversely affect the environment, human health, and economic activity. One way to mitigate these effects is a final disinfection step using ultraviolet light (UVL). The advantages of UVL disinfection, when compared to the more traditional chlorine, include no chlorinated by-products, no chemical residual, and relatively compact size. The design of most UV reactors is complex. It involves lamp selection, power supply design, optics, and hydraulics. In general, medium pressure lamps are more compact, powerful, and emit over a wider range of light than the more traditional low pressure lamps. Low pressure lamps, however, may be electrically more efficient. In UV disinfection, the fraction of surviving organisms (e.g. E. coli) will decrease exponentially with increasing UV dose. However, the level of disinfection that can be achieved is often limited by particle-associated organisms. Efforts to remove or reduce the effects of wastewater particles will often improve UV disinfection effectiveness. Regrowth, photoreactivation, or dark repair after UV exposure are sometimes cited as disadvantages of UV disinfection. Research is continuing in this area, however there is little evidence that human pathogens can photoreactivate in environmental conditions, at doses used in wastewater treatment. The UV disinfection of combined sewer overflows, a form of wet weather pollution, is challenging and remains largely at the research phase. Pre-treatment of combined sewer overflows (CSOs) with a cationic polymer to induce fast settling, and a low dose of alum to increase UV transmittance, has shown promise at the bench scale.