Automatic Monitoring Stations Research Articles

Analysis of Pollution Characteristics and Influencing Factors of Main Pollutants in the Atmosphere of Shenyang City

Air pollution is one of the most concerning environmental problems in cities. Hourly data on pollutant concentrations from 11 automatic atmospheric monitoring stations and meteorological data in Shenyang from 2017 to 2019 were used to analyze the spatio-temporal variation rules of CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), O3 (ozone), PM2.5 and PM10 (PM particles with an aerodynamic diameters of not more than 2.5 µm and 10 µm) and their relationships with meteorological parameters. Meanwhile, the regional transmission route of pollutants was analyzed by the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model. The results showed that the concentration of O3 in the northern area of the city was higher than that in the south; CO, SO2 and NO2 were relatively high in the urban center; and PM2.5 and PM10 were relatively high in the southwest. The average concentration of pollutants was lowest in 2019. The concentration of O3 was the highest in spring, while CO showed no significant variations between different seasons. The remaining pollutant concentrations appeared to be high in winter and low in summer. The cumulative concentrations of the six pollutants were the highest in March, and relatively low in July–September. The diurnal concentration variations of O3, CO and SO2 exhibited a “single peak,” while others showed a “double peak and double valley.” Temperature was positively correlated with O3 concentration and negatively correlated with others. Wind speed was negatively correlated with the concentration of PM2.5, NO2, and O3. The air quality of the main urban area in spring and summer was mainly affected by the coastal air flow, while it was mostly affected by the northwest air flow in autumn and winter.

Increased ozone levels during the COVID-19 lockdown: Analysis for the city of Rio de Janeiro, Brazil

The first COVID-19 case in Brazil was confirmed on February 25, 2020. Partial lockdown measures came into force in the city of Rio de Janeiro, Brazil, on March 23. While CO and NO2 levels showed significant reductions, PM10 levels were only reduced in the first partial lockdown week. By contrast, ozone levels increased in all studied locations. In this study, the factors leading to this behavior were analyzed. Monitoring data obtained at two automatic monitoring stations showed higher ratios between non-methane hydrocarbons and nitrogen oxides (NMHC/NOx) during the partial lockdown (up to 37.3%). The increase in ozone concentrations during the social distancing measures could be attributed to the increase in NMHC/NOx ratios since atmospheric chemistry in Rio de Janeiro is under VOC-controlled conditions. However, the increase was higher when air masses arrived from the industrial areas, not only because of the higher NMHC/NOx ratios, but also because the reactivity of VOC was highly increased by these air masses, which are rich in aromatic compounds.

Application of Cloud Computing in Geological Hazard Early Warning System

China’s geomorphology is complex and changeable. Geological hazards occur frequently. Landslides, mudslides and other geological hazards seriously threaten the lives and property of the people. Monitoring and early warning of geological hazards to reduce the impact of disasters is an urgent problem at this stage. In order to ensure the safety of people’s lives and property, the design and development of real-time monitoring and early warning systems are the most effective means of preventing and controlling disasters. This article is based on the Microsoft cloud computing platform. The geological data collected by the automatic monitoring station is transmitted to the cloud server through GPRS DTU and stored in SQL Server 2008. Generate Web Service through VS2010 platform, use ASP.NET technology to build dynamic website to display early warning details, complete Web Service call through Android smartphone platform, conduct real-time geological disaster warning information query, and provide users with cloud platform-based geological disaster warning system. After analyzing the causes of geological hazards, a model of geological hazard influencing factors, a model for calculating the probability of hazards in geological structures, and a process for processing geographic information data were designed. Using the data of the extracted GIS spatial database to predict geological disasters.

A study of daily variations of the outdoor background radiation measured in continuous mode in Federation of Bosnia and Herzegovina

Systematic monitoring of environmental radionuclides with the aim of early warning in emergency situations in Federation of Bosnia and Herzegovina (FB&H) was established in 2004. Environmental radiation monitoring network includes six automatic monitoring stations over the FB&H territory. This paper deals with the first study of ambient dose equivalent rates collected over the period of 2012–2017 with the main objective to investigate the temporal and spatial variations in the outdoor background radiation. The correlation analysis between the continuously acquired gamma dose rates and the simultaneous meteorological records on a daily basis contributed to a better interpretation of daily variations of the measured data. Derivation of the net ambient dose equivalent rates and the terrestrial radiation component from routine monitoring data was carried out as a prerequisite for adequate monitoring of background radiation. In addition, the terrestrial background component was estimated from the soil radionuclides at the same monitoring sites. The correlation analysis in a form of bivariation statistics between activity concentration of each pair of primordial radionuclides in the soil samples show that primordial radionuclides are not uniformly distributed over the FB&H territory. The mean values with the combined standard uncertainties for the terrestrial component evaluated from the soil radionuclides of (74.4 ± 12.5) nSv/h and from routine monitoring data of (81.8 ± 17.9) nSv/h are within the ranges reported for East and South European countries. The sources of statistical and systematic uncertainties were analyzed. The applied procedure of the terrestrial radiation component evaluation based on the daily monitoring data could be of complementary significance related to the existing evaluation procedures with a smaller sampling time of monitoring records. The results of the present study indicate that the acquired monitoring data are reliable enough to correctly assess the radiological situation in FB&H. In case of an emergency situation, the results obtained could be useful for a better identification of contaminated area.

Major Effects of Alkalinity on the Relationship Between Metabolism and Dissolved Inorganic Carbon Dynamics in Lakes

Several findings suggest that CO2 emissions in lakes are not always directly linked to changes in metabolism but can be associated with interactions with the dissolved inorganic carbon equilibrium. Alkalinity has been described as a determining factor in regulating the relative contributions of biological and inorganic processes to carbon dynamics in lakes. Here we analyzed the relationship between metabolic changes in dissolved oxygen (DO) and dissolved inorganic carbon (DIC) at different timescales in eight lakes covering a wide range in alkalinity. We used high-frequency data from automatic monitoring stations to explore the sensitivity of DIC to metabolic changes inferred from oxygen. To overcome the problem of noisy data, commonly found in high-frequency measurements datasets, we used Singular Spectrum Analysis to enhance the diel signal-to-noise ratio. Our results suggest that in most of the studied lakes, a large part of the measured variability in DO and DIC reflects non-metabolic processes. Furthermore, at low alkalinity, DIC dynamics appear to be mostly driven by aquatic metabolism, but this relationship weakens with increasing alkalinity. The observed deviations from the metabolic 1:1 stoichiometry between DO and DIC were strongly correlated with the deviations expected to occur from calcite precipitation, with a stronger correlation when accounting also for the benthic contribution of calcite precipitation. This highlights the role of calcite precipitation as an important driver of CO2 supersaturation in lakes with alkalinity above 1 meq L−1, which represents 57% of the global area of lakes and reservoirs around the world.

Short-term effects of air pollution on exacerbations of allergic asthma in Užice region, Serbia.

IntroductionMany time-series studies have shown a positive association between air pollution and asthma exacerbation. However, till now only one study in Serbia has examined this relationship.AimTo examine the associations between air pollution and asthma emergency department (ED) visits in the Užice region, Serbia.Material and methodsA time-stratified case-crossover design was applied to 424 ED visits for asthma exacerbation that occurred in the Užice region, Serbia, in 2012–2014. Data about ED visits were routinely collected in the Užice Health Centre. The daily average concentrations of particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), and black carbon (BC) were measured by automatic ambient air quality monitoring stations. Odds ratios and their corresponding 95% confidence intervals were estimated using conditional logistic regression adjusted for the potential confounding influence of weather variables (temperature, humidity and air pressure).ResultsStatistically significant associations were observed between ED visits for asthma and 3-day lagged exposure to BC (OR = 3.23; 95% CI: 1.05–9.95), and between ED visits for asthma with coexisting allergic rhinitis and 0-day lag exposure to NO2 (OR = 1.57; 95% CI: 0.94–2.65), 2-day lag exposure to SO2 (OR = 1.97; 95% CI: 1.02–3.80), and 3-day lag exposure to PM10 (OR = 2.38; 95% CI: 1.17–4.84).ConclusionsExposure to ambient air pollution in the Užice region increases the risk of ED visits for asthma, particularly during the heating season.

Analysis of NOx Pollution Characteristics in the Atmospheric Environment in Changchun City

Nitrogen oxide (NOx) pollution has become one of the most challenging problems in China in the past 20 years. In this study, on the basis of the Jilin Province Atmospheric Environmental Quality Bulletin and hourly NOx data from the Atmospheric Environment Automatic Monitoring Station in Changchun, temporal and spatial variations in NOx concentration in the province and Changchun and their relationships with various pollutants and meteorological factors were analyzed. The results show that Changchun had the highest NOx concentration of all cities in the province, with a high concentration in the center and a low concentration in the east and west. The areas with high NOx concentrations in Changchun were mainly distributed in urban centers, and the concentration in the northern part of the city was higher than that in the south. The seasonal variation and average daily variation in NOx concentration in Changchun had a bimodal distribution, and the NOx concentration in autumn and winter was higher than that in spring and summer. The maximum monthly average concentrations of NOx and nitric oxide (NO) were reached in October, and the maximum monthly average concentration of nitrogen dioxide (NO2) was reached in March. The average daily variation in NOx concentration first peaked at 07:00–08:00 in the morning, and the second peak occurred between 20:00 and 22:00 at night. The NOx concentration in Changchun was positively correlated with NO2, NO, PM2.5 (fine particulate matter), PM10 (particulate matters), CO (carbon monoxide), and pressure, and it showed a significant negative correlation with O3, temperature, wind speed, and humidity.

Obtaining and using data of operational monitoring of atmospheric air

In order to control the chemical composition of atmospheric air in cities located in the zone of influence of petrochemical enterprises, automatic atmospheric air monitoring stations (ASKAV) are installed. For the effective use of experimental data, the authors of the article developed a data collection system with ASKAV, in which the air control in residential quarters and the gas mixture at the source of organized emissions are synchronized. The analysis of data on the concentration of pollutants in the atmospheric air of the Sterlitamak obtained from ASKAV, on the basis of which a list of marker substances for enterprises of the city was compiled. Priority sources of air pollution with marker substances during a different wind regime were identified. The authors developed models for changing the concentration of marker substances in the air using the method of factor regression. Based on the results of the cross-correlation function, the time of movement of the contaminated gas cloud from the source of emission to the residential area of the city is obtained. Conclusions are made about the effectiveness of the developed air monitoring system and the field of application of the acquired models.

Stationary and mobile particulate matter concentration measurement in Miskolc

A stationary and mobile device based on a low-cost dust sensor (Plantower PMS7003) was used to study particulate matter (PM) concentration in Miskolc. The stationary device was placed at the automatic monitoring station of the National Air Quality Measurement Network (OLM) in Martintelep, Miskolc. The mobile device was used to walk 4 streets along a square route with a total length of 800 m. Measurements were made on two days (September 9 and 11) between 7am and 9am, which according to preliminary experience coincided with the morning rush hour. The access route includes the OLM monitoring station, a busy road, two bypasses, a parking lot, and a smaller roadway blocked by traffic. The measured PM1, PM2.5, PM10 concentrations of the mobile device were interpolated on a circuit-by-circuit basis in ArcGIS, and conclusions were drawn for the area. The concentration of stationary and mobile devices was compared.

Methodical problems of monitoring of fine particulate matters in atmospheric air of residential areas

Inhalation of fine particulate matters (PM and PM ) poses a threat for the health of population. Purpose of the study the analysis of the monitoring of fine particulate matters in the atmospheric air of Saint-Petersburg and identification of the main problems of the monitoring. Research methods methods of scientific hypothetical deductive cognition, sanitary-statistical methods, general logical methods and approaches of researches: analysis, synthesis, abstracting, generalization, induction. Results. The article represents the analysis of the monitoring of fine particulate matters in the atmospheric air of Saint- Petersburg. Only 11 in automatic monitoring stations out of 22 there is carried out the control of fine particulate matters: in 7 - PM and PM, and in 4 - PM The average year concentrations were below MAC in all the stations. The maximum concentrations achieved 3 MAC, but the repeatance of cases of exceedence of concentrations more than MAC was very rare. On the average of the city concentrations of PM were decreased from 0,8 MAC in 2006 and 1,1 MAC in 2007 to 0,5 MAC in 2013-14. The executed analysis revealed main problems of the monitoring of fine particulate matters in the Russian Federation. They include the absence of the usage 1of the officially approved methods of controlling of PM and PM in the atmospheric air until March 1, 2016, lack of the modern equipment for measurement of fine particulate matters. Conclusions. Therefore, the state of the monitoring of fine particulate matters in the atmospheric air in the Russian Federation fails to be satisfactory. It is necessary to improve system of the monitoring, create modern Russian appliances, methods and means for measurement of fine particulate matters concentrations in the atmospheric air.

Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors

With Changchun’s economic development, atmospheric particulate pollution has become a significant challenge in Changchun. The spatiotemporal patterns of particulate matter emissions are an inherent characteristic for particulate matter emissions. By using hourly PM (particulate matter) mass concentration measured at 10 atmospheric automatic monitoring stations and meteorological parameters, the spatiotemporal distribution characteristics of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters of Changchun have been analyzed. Pollution pathways and source distribution were investigated using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model and cluster analysis. Results indicated that the quarterly average PM2.5 and PM10 mass concentrations in Changchun were higher in the first quarter and the fourth quarter. PM concentrations observed in all seasons generally exhibited two peaks, at 07:00–10:00 and 21:00–23:00, with the exception of PM10 in spring. PM pollution was concentrated mainly in the central, northern, and western areas of Changchun in most seasons, mainly due to anthropogenic activities and soil dust transported outside the region. PM concentrations were negatively correlated with relative humidity and temperature. PM2.5 concentrations were negatively correlated with wind speed, while PM10 concentrations were positively correlated with wind speed. The results of backward trajectory clustered showed that the northwest airflow had the greatest impact on PM of Changchun, except summer.

Temporal evolution characteristics of PM2.5 concentration based on continuous wavelet transform

Fine particulate matter (PM2.5) is an important haze index, and the researches on the evolutionary characteristics of the PM2.5 concentration will provide a fundamental and guiding prerequisite for the haze prediction. However, the past researchers were usually based on the overall time-domain evolution information of PM2.5. Since the temporal evolution of PM2.5 concentration is nonstationary, previous studies might neglect some important localization features that the evolution has various predominant periods at different scales. Therefore, we applied the wavelet transform to study the localized intermittent oscillations of PM2.5. First, we analyze the daily average PM2.5 concentration collected from the automatic monitoring stations. The result reveals that the predominant oscillation period does vary with time. There exist multiple oscillation periods on the scale of 14–32 d, 62–104 d, 105–178 d and 216–389 d and the 298d is the first dominant period in the entire evolutionary process. Moreover, we want to figure out whether the temporal characteristics of PM2.5 in the days with heavy haze also have localized intermittent periodicities. We select the hourly average PM2.5 concentration in 120 h when the haze pollution is serious. We find that the principal period has experienced two abrupt shifts and the energy at the 63-hour scale is the most powerful. The results in these two independent analyses come into the same conclusion that the multiscale features shown in the temporal evolution of PM2.5 cannot be ignored and may play an important role in the further haze prediction.

The Wireless Acquisition Data System Simulator Design on Automatic Weather Monitoring Station

The Wireless Acquisition Data System Simulator Design on Automatic Weather Monitoring Station is means of collecting, monitoring, and controlling some long distance subsystems. Automatic weather monitoring station is device for receiving data by a censor in use to gauge some weather parameters. In this Research we develop an automatic weather monitoring station design simulator by creating a subsystem in the form of prototype, using Digital Pin and Analog Pin from the microcontroller. Sensor characteristics are considered to measure several weather parameters. The principles of a system function are it will have to collect data from some subsystems to accommodate in a database, able to control and deliver instructions to the subsystem. The communication system makes the use of radio frequency by means of Frequency Shift Keying (FSK) method through Handy Talky (HT), picked out because advantages of lower power consumptions and long range of transmission. Additional advantages are, although catastrophes, data communication using HT allows to keep fast transmission. The transmission of data using HT media require a signal converter by which HT is operated by analogue signal (sound), while censor and microcontroller by digital signal and, therefore, Modulation and Demodulation techniques are necessary. The Modulation functions to convert digital signal into analogue, and just the opposite to the Demodulation, the Modulation and Demodulation processes is using IC TCM3105. The results of the trial suggest that system design has proper functions, the communication process in this system is two-ways communication, the process was fast working in either transmitter mode or receiver mode.

In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches

The current compliance networks of automatic air-quality monitoring stations in large urban environments are not sufficient to provide spatial and temporal measurement resolution for realistic assessment of personal exposure to pollutants. Small low-cost sensor platforms with greater mobility and expected lower maintenance costs, are increasingly being used as a supplement to compliance monitoring stations. However, low-cost sensor platforms usually provide data with uncertain precision. To improve the precision, these sensor platforms require in-field calibration. Our paper aims to demonstrate that data from each individual sensor system can be corrected using that sensor system's own data to achieve much improved data quality compared to a reference. However, in this procedure, there are practical difficulties such as individual sensor outputs from the multi-sensor system not being sufficiently available due to malfunctions for instance. We explore how this can be dealt with. In our opinion, this is a novel approach, of practical importance both to users and manufacturers. We present a detailed comparative analysis of Linear Regression (univariate), Multivariate Linear Regression and Artificial Neural Networks used with a specific aim of calibrating field-deployed low-cost CO and O3 sensors. For Artificial Neural Network models, the performance of three common training algorithms was compared (Levenberg-Marquardt, Resilient back-propagation and Conjugate Gradient Powell-Beale algorithm). Data for this study were obtained from two campaigns conducted with 25 multi-sensor AQMESH v.3.5 platforms used within the activities of the CITI-SENSE project. The platforms were co-located to reference gas monitors at the Automatic Monitoring Station Stari Grad, in Belgrade, Serbia. This paper demonstrates that Multivariate Linear Regression and Artificial Neural Network calibration models can improve the output signal. This improvement can be measured by changes in the median and interquartile ranges of statistical parameters used for model evaluation. Artificial Neural Networks showed the best results compared to Linear Regression and Multivariate Linear Regression models. The best predictors for CO, in addition to CO low-cost sensor data, were PM2.5 and NO2, while for O3, in addition to O3 low-cost sensor data, the most suitable input predictors were NO and aH. Based on residual error analysis, we have shown that for CO and O3, a certain range of concentrations exists in which calibrated values differ by less than 10% from the reference method results. In addition, it was noted that for all models, CO sensors consistently showed lower variability between platforms compared to O3 sensors.

Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece

Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.

Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations

Airborne pollen is a recognized biological indicator and its monitoring has multiple uses such as providing a tool for allergy diagnosis and prevention. There is a knowledge gap related to the distribution of pollen traps needed to achieve representative biomonitoring in a region. The aim of this manuscript is to suggest a method for setting up a pollen network (monitoring method, monitoring conditions, number and location of samplers etc.). As a case study, we describe the distribution of pollen across Bavaria and the design of the Bavarian pollen monitoring network (ePIN), the first operational automatic pollen network worldwide.We established and ran a dense pollen monitoring network of 27 manual Hirst-type pollen traps across Bavaria, Germany, during 2015. Hierarchical cluster analysis of the data was then performed to select the locations for the sites of the final pollen monitoring network. According to our method, Bavaria can be clustered into three large pollen regions with eight zones. Within each zone, pollen diversity and distribution among different locations does not vary significantly. Based on the pollen zones, we opted to place one automatic monitoring station per zone resulting in the ePIN network, serving 13 million inhabitants. The described method defines stations representative for a homogeneous aeropalynologically region, which reduces redundancy within the network and subsequent costs (in the study case from 27 to 8 locations). Following this method, resources in pollen monitoring networks can be optimized and allergic citizens can then be informed in a timely and effective way, even in larger geographical areas.

Evaluation of the Impact of Black Carbon on the Worsening of Allergic Respiratory Diseases in the Region of Western Serbia: A Time-Stratified Case-Crossover Study.

Background and Objectives: Many epidemiological studies have shown a positive association between black carbon (BC) and the exacerbation of allergic rhinitis and allergic asthma. However, none of the studies in Serbia examined this relationship yet. The aim of this study was to examine the associations between BC and emergency department (ED) visits for allergic rhinitis and allergic asthma in the Užice region of Serbia. Materials and Methods: A time-stratified case-crossover design was applied to 523 ED visits for allergic rhinitis and asthma exacerbation that occurred in the Užice region of Serbia between 2012–2014. Data regarding ED visits were routinely collected in the Health Center of Užice. The daily average concentrations of BC were measured by automatic ambient air quality monitoring stations. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were estimated using conditional logistic regression adjusted for the potential confounding influence of weather variables (temperature, humidity, and air pressure). Results: Statistically significant associations were observed between ED visits for allergic rhinitis and 2-day lagged exposure to BC (OR = 3.20; CI = 1.00–10.18; p = 0.049) and allergic asthma and 3-day lagged exposure to BC (OR = 3.23; CI = 1.05–9.95; p = 0.041). Conclusion: Exposure to BC in the Užice region increases the risk of ED visits for allergic rhinitis and asthma, particularly during the heating season.

Analysis of the Spatial–Temporal Variation of the Surface Ozone Concentration and Its Associated Meteorological Factors in Changchun

Ozone (O3) pollution has become one of the most challenging problems in China, and high O3 concentrations have been a major air quality issue in Changchun. Based on continuous observation data of surface ozone concentrations from ten automatic air monitoring stations and meteorological data from the meteorological bureau in Changchun, the temporal and spatial variations of the O3 concentration and its relationships with meteorological factors were analyzed by correlation analysis during the period of 2013–2017. The results showed the following: A single apex model of the annual mean O3 concentrations of the daily maximum 8 h average (MDA8) was found from the data for 2013 to 2017 in Changchun, with the highest MDA8 O3 concentrations in 2015 and a slight decline from then until 2017. The O3 concentrations in the suburban areas and the south of Changchun were higher than those downtown and north of the city. The seasonal variation of O3 concentrations was obvious, following the order summer > spring > autumn > winter, which was similar to the results of neighboring cities and provinces in Changchun. The days on which O3 concentrations exceeded the standard were concentrated in summer and spring, and the total number of ozone excess days was 91 days; the maximum number of ozone excess days was in 2015. The O3 concentration exceeded the standard in Changchun mainly in March–August, and its monthly mean value curve showed a bimodal type in which the highest values appeared in May and July, while the lowest values appeared in December. The diurnal pattern of ozone showed a single peak mode, and the peak value usually appeared at 14:00–16:00 while the minimum value appeared at 07:00–08:00. O3 concentrations in Changchun and the six selected pollutants CO, NO, NO2, NOx, PM10, and PM2.5 were negatively correlated. Higher temperature is a necessary synoptic condition for ozone pollution in Changchun: when the temperature rose, O3 concentrations increased significantly; further, O3 concentrations were negatively correlated with relative humidity and atmospheric pressure and were positively correlated with temperature and solar radiation. The O3 concentrations were highest when the wind scale approached 14~20 km/h and the wind direction was S. Combined with the research results in the surrounding areas of Changchun, it is indicated that there may be an ozone contribution from south of Changchun through long-range pollution transport and tropospheric subsidence.

Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Spatial and Temporal Distribution Characteristics and Fugitive Dust Emission of Building Sites in Beijing

Because of the continuous expansion of the city and the evacuation of non-core functions, the city pattern of Beijing has gradually changed in recent years. Based on many construction projects, building sites are widely distributed within the city. Therefore, fugitive dust emission is an important factor affecting the air quality in Beijing. This article focuses on the plain area in Beijing. Remote sensing was used to extract building sites from 2013 to 2017, analyze the spatial and temporal distribution characteristics of the building sites, estimate the fugitive dust emission, and propose a grid-based emission classification management program. Research shows that the building sites form a belt surrounding the central urban area, mainly distributed in the urban-rural integration area. From 2013 to 2017, the area of the building sites first decreased and then increased. After 2015, the center of the building sites in Beijing shifted to the southeast and started to show an uneven distribution. The area of the building sites is positively correlated with the PM10 concentration. The correlation coefficients of 23 automatic ground monitoring stations are all above 0.80. Combined with the building site activity levels and emission factors, the estimated air pollutant emissions of TSP, PM10, and PM2.5 from building sites in 2017 were 39.5×104, 19.4×104, and 4.0×104 t, respectively. The emission intensity of the pollution in the grid has developed towards polarization, which further increased the emission intensity of high-strength pollution grids. Based on standardized PM10 grid emissions of building suites, the plain area of Beijing can be divided into five categories:lower pollution, low pollution, medium pollution, high pollution, and higher pollution.