Measurements of speciated atmospheric mercury play a key role in identifying mercury behavior in the atmosphere. In this study, we measured speciated atmospheric mercury, including gaseous elemental mercury (GEM), reactive gaseous mercury (RGM), and particulate bound mercury (PBM) (<2.5 μm), in 2015 and 2016 at an urban site in Beijing, China. The mean concentrations of GEM, RGM, and PBM were 4.70 ± 3.53 ng m−3, 18.47 ± 22.27 pg m−3, and 85.18 ± 95.34 pg m−3, respectively. The concentration of PM2.5 significantly affected the distribution of reactive mercury between the gaseous and particulate phases. With the raising of PM2.5 levels, PBM concentrations increased, on the contrary, the concentrations of RGM decreased gradually. The mean concentration of PBM during air-pollution events was more than three times that during clear days. During days with air pollution, the relative humidity significantly affected the gas-particle partitioning of reactive mercury. The linear relationships between gas-particle partitioning coefficient and meteorological factors (air temperature and relative humidity) were obtained over the four seasons. The data also showed that the gas-particle partitioning coefficient of reactive mercury was related to particle composition (e.g., Cl−, BC). The data present in this paper suggested the influence of anthropogenic emissions on reactive mercury in Beijing urban. And the findings will contribute to understand the gas-particle partitioning of reactive mercury and its influencing factors with complex urban pollution.

As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM₂.₅, PM₁₀, and PMcₒₐᵣₛₑ (PM₁₀ - PM₂.₅) and PM₂.₅ to PM₁₀ ratios (PM₂.₅/PM₁₀) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM₂.₅ and PM₁₀ decreased from 48 to 42 μg m⁻³ and from 88 to 84 μg m⁻³, respectively, and the annual mean concentrations were 45 μg m⁻³ (PM₂.₅) and 84 μg m⁻³ (PM₁₀) during the time period (2015–2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PMcₒₐᵣₛₑ concentrations were highest due to dust. The PM₂.₅/PM₁₀ ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.

Burial in sediments is a crucial way to reduce mobilization and risks of hydrophobic organic contaminants (HOCs), but ability of sediments to bury HOCs may be altered if the environment is changed. Whether the ability of sediments to bury HOCs has been affected by climate change remains largely unclear. We excluded the impacts of anthropogenic emissions and eutrophication from that of climate change, and for the first time found that not only the rising surface air temperature but also the declining wind speed and the reducing days with precipitation had weakened the ability of Chinese lakes to bury 16 polycyclic aromatic hydrocarbon (PAHs) by 69.2% ± 9.4%–85.7% ± 3.6% from 1951 to 2017. The relative contributions of the climatic variables to the reduced burial ability depended on the properties of the PAHs, and lakes. Burial ability of the PAHs responded differently to climate change, and was correlated to their volatilization and aqueous solubility, and lake area, catchment area/lake area ratio, and water depth. Our study suggests that not only the rising surface air temperature but also the declining wind speed and the reducing days with precipitation can undermine global efforts to reduce environmental and human exposure to PAHs.

Fast growth of air pollution was experienced in China during the past decades, resulting in extremely large aerosol radiative forcing with up to ten times high compared to the global averages. The responses of surface air temperature to aerosol radiative effects range from − 0.1 to 1.1 K across China, with strong spatial and seasonal variations. Aerosol interaction with clouds can also affect radiation and precipitation. Under high pollution conditions, the frequency of heavy rain increases while the frequency of light rain decreases. Aerosols share common emission sources with CO₂, implying that reducing common sources can be another manifestation of the interaction between aerosol and climate. Air pollution controls will contribute to the climate change mitigation by reducing CO₂ emissions stemming from controls of pollution sources. On the other hand, potential co-benefits in reducing air pollution also come from the mitigation of climate change by reducing CO₂ emission that potentially affects the aerosol concentrations. It is suggested that the PM₂.₅ (fine particulate matter with an aerodynamic diameter of 2.5 μm or less) concentration tends to decrease under future climate control scenarios. Such co-benefits are mostly associated with the reduction of common sources of CO₂ and aerosol (noted as emission-driven interaction), rather than the variation of meteorological conditions associated with future climate change (noted as meteorology-driven interaction). Compared to emission-driven interaction, the impact by meteorology-driven interaction on aerosols is relatively smaller and varies geographically as the PM₂.₅ concentration is likely to increase in northern China while decrease in eastern China. The current review suggested that the co-benefits of reducing CO₂ and aerosol pollutant in China, and their response in the context of future climate change, are worthy of further research.

The latest research focused on the analysis of algal growth and the dynamics of their growth use the laser diffraction technique, enabling determination of the volume fraction of suspended particles with specific diameters in aqueous solution as well as their fractal dimensions. This study focuses on the possibility of using a laser granulometer to assess the growth dynamics of algae growing in treated wastewater in a hydroponic system, supported by artificial lighting with the use of light-emitting diodes (LEDs). On the basis of the measurements, the fractal dimension (Df) of algae was determined. An attempt was made to apply the modified Avrami equation describing the crystallization process for the analysis of algae growth dynamics in wastewater. Presented results show that the fractal dimension of suspended matter, largely created by algae, in the case of additional lighting of the hydroponic system at night, takes lower values (Df ~ 1.0) than in sewage without additional light source (Df ~ 2.0). In each measurement series, the fractal dimension of particles in the tank with lighting in the end of the experiment was about 33–43% lower than in the tank without LEDs. The analysis of changes in particle diameters calculated on the basis of Avrami equation largely corresponds with the stages of algae growth. During the measurement series with lower air temperatures, the growth of algae in the tank with additional light was faster than in the tank without LEDs. The obtained information can be the basis for determining the effective method of removing algae from wastewater treated in the hydroponic system, before they are discharged to the receiver in order to prevent the outflow of increased concentrations of total suspended solids.

This article identifies environmental factors that explain most of the dynamic year-to-year changes in mercury concentrations of young-of-year (YOY) yellow perch (Perca flavescens) in study reservoirs. Mercury concentrations in fish, collected each fall, were measured for 9 years in four reservoirs in northeastern Minnesota. Three to 4 years of data were also obtained for two natural lakes and one other reservoir. Average annual concentrations varied considerably from year to year with a mean change of 39% between consecutive years across all lakes. Those averages show a similar time trend for each lake over the years and suggest that important factors influencing mercury bioaccumulation change annually and are also experienced in common over the study region. Three factors satisfying that description are precipitation depth, water level, and average air temperature. This article reveals that all three have statistically significant correlations with observed mercury concentrations. Moreover, multiple regressions indicate that maximum water levels and average air temperatures explain most of the observed variations. Regressions employing precipitation depth and temperature are less significant.

The PM₂.₅ as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM₂.₅ concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM₂.₅ concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM₂.₅ concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM₂.₅ concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM₂.₅ concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM₂.₅ concentration than summer season (40%). In winter, PM₂.₅ concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM₂.₅ concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM₂.₅ concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM₂.₅ concentration in summer. In this season, the air temperature parameter showed a high correlation with PM₂.₅ concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer.

This paper involves discovering effective and better reaction of the diesel engine at various velocities by having ideal values in a short period. Therefore, gene expression programming is used for modeling and presenting governing expression for the related factors. The effective parameters consist of engine speed, intake air temperature, rate of air over fuel, fuel mass, NOx emission, mechanical efficiency, and immediate infusion diesel engine used as a part of demonstrating. Gene expression programming and its values exactly predict output results and present precise formula. Moreover, the sensitivity analysis was performed to recognize the effectiveness of the factors for reducing NOx and increasing mechanical efficiency. In the sensitivity analysis, the methods such as partial correlation coefficient, standard regression coefficient, and the Sobol’-Jansen and distributed evaluation of local sensitivity analysis are all used. The obtained results show that air/fuel rate is more influential factor in both NOx emission and mechanical efficiency. Moreover, the difference between results of standard regression or partial correlation coefficients and Sobol’-Jansen or distributed evaluation methods is in nonlinearity effect or interactions among the factors.

High-frequency and reliable data on cyanobacteria blooming over a long time period is crucial to identify the outbreak mechanism of blooms and to forecast future trends. However, in cloudy and rainy areas, it is difficult to retrieve useful satellite images, especially in the rainy season. To address this problem, we used data from the HJ-1/CCD (Chinese environment and disaster monitoring and forecasting satellite/charge coupled device), GF-1/WFV (Chinese high-resolution satellite/wide field of view), and Landsat-8/OLI (Operational Land Imager) satellites to generate a time series of the bloom area from 2009 to 2016 in Dianchi Lake, China. We then correlated the responses of bloom dynamics to meteorological factors. Several findings can be drawn: (1) a higher bloom frequency and a larger bloom area occurred in 2011, 2013, and 2016, compared to the other years; (2) the frequency of blooms peaked in April, August, and November each year and expanded from north to south starting in July; (3) air temperature in spring and sunshine hours in summer greatly correlated to the yearly bloom area; (4) wind speed and sunshine hours strongly affected the short-term expansion of blooms and thereafter influenced the monthly bloom scale; and (5) rainfall had a strong short-term influence on the occurrence of blooms. Cyanobacteria blooms often occurred when wind speeds were less than 2.35 ± 0.78 m/s in the dry season and 2.01 ± 0.75 m/s in the rainy season, when there were 48 to 72 h of sunshine in the dry season and 35 to 57 h of sunshine in the rainy season, and when there was more than 10 mm of daily precipitation.

This study examined the sensitivity of reference crop evapotranspiration (ET₀) to climatic variables in a humid region in Iran. ET₀ was estimated using the FAO-56 Penman–Monteith (PMF-56), Blaney–Criddle (BC), and Hargreaves–Samani (HG) methods. Sensitivity analysis was performed by two distinct methods which were (i) changing the value of a certain climatic parameter in a range between ± 20% of its long-term mean with an increment of 5%, and calculating the percentage of change in ET₀, while the other parameter values were kept constant; and (ii) calculating the sensitivity coefficients (SCs) for each of the climatic variables. For each of the climatic parameters, the Iso-SC maps were plotted using the Arc-GIS software. Results indicated that the most sensitive parameter for ET₀ was the maximum air temperature (Tₘₐₓ) by PMF-56 and HG methods. Increasing Tₘₐₓ up to 20% led to an increase in ET₀ between 8.5 and 15%, at the selected stations by PMF-56. In contrast, the less sensitive parameter for ET₀ was the minimum air temperature (Tₘᵢₙ) for PMF-56 and Tₘₑₐₙ for HG. For PMF-56, increasing the minimum relative humidity (RHₘᵢₙ) to 20% led to a decrease in ET₀ in the range between 0.5 and 5%. The highest values of SC in the cases of Tₘₐₓ and Tₘᵢₙ were found to be equal to 0.8 and 0.53, respectively. Similarly, the SC in the case of RHₘᵢₙ varied between − 0.29 and − 0.0038. This range for wind speed was between 0.06 and 0.22 and in the case of sunshine hours it was between 0.272 and 0.385. These findings would be useful in the scientific management of water resources in the region.