Assessment of spatial variations of particulate matter (PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript>) in Bahrain identified by air quality index (AQI)

The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM₁₀ and PM_2.5), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM₁₀ and PM_2.5 are the most critical air pollutants in Bahrain with PM_2.5 prevailing during January 2012 and PM₁₀ prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM₁₀ concentrations were observed in the Northern Governorate while the highest PM_2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM_2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM_2.5/PM₁₀ ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM₁₀ and PM_2.5 in comparison to August 2012.     

A study of PM<Subscript>2.5</Subscript> and PM<Subscript>10</Subscript> concentrations in the atmosphere of large cities in Gansu Province,China, in summer period

Due to rapid economic growth of the country in the last 25 years, particulate matter (PM) has become a topic of great interest in China. The rapid development of industry has led to an increase in the haze created by pollution, as well as by high levels of urbanization. In 2012, the Chinese National Ambient Air Quality Standard (NAAQS) imposed ‘more strict’ regulation on the PM concentrations, i.e., 35 and 70 μg/m³ for annual PM_2.5 and PM₁₀ in average, respectively (Grade-II, GB3095-2012). The Pearson’s correlation coefficient was used to determine the linear relationship of pollution between pollution levels and weather conditions as well as the temporal and spatial variability among neighbouring cities. The goal of this paper was to investigate hourly mass concentration of PM_2.5 and PM₁₀ from June 1 to August 31, 2015 collected in the 11 largest cities of Gansu Province. This study has shown that the overall average concentrations of PM_2.5 and PM₁₀ in the study area were 26 and 66 μg/m³. In PM_2.5 episode days (when concentration was more than 75 μg/m³ for 24 hrs), the average concentrations of PM_2.5 was 2–3 times higher as compared to non-episode days. There were no observed clear differences during the weekday/weekend PM and other air pollutants (SO₂, NO₂, CO and O₃) in all the investigated cities.     

Experimental and numerical analyses of particulate matter concentrations in underground subway station

The purpose of this paper was to perform the experimental and numerical analyses of PM₁₀ and PM_2.5 concentrations in Imam Khomeini (IKH) underground subway station in Tehran. The aim was to provide fundamental data in order to fulfill workers and passengers respiratory health necessities. Experimental measurements was done at three different locations (entrance, middle and exit) inside the platform and also outdoor ambient of the station. The Dust-Trak was applied to measure continuous PM_2.5 and PM₁₀ concentrations at a logging interval of 30 s. The measurements were recorded during rush hours (8:00 am–12:00 pm) for one week per each season from June 2015–June 2016.Moreover, computational fluid dynamic (CFD) simulation was done for the platform of the above station and the necessary boundary conditions were provided through field measurements. Those basic parameters which were considered for numerical analysis of particulate matters concentrations included air velocity, air pressure and turbulence. Furthermore, the piston effect caused by train movement inside the station provided natural ventilation in the platform. The results showed that seasonal measured PM_2.5 and PM₁₀ indoor concentrations had a variety range from 40–98 µg/m³ to 33–102 µg/m³, respectively, and were much higher than national indoor air quality standard levels. Meanwhile, PM_2.5 and PM₁₀ concentrations in the IKH underground subway station were approximately 2.5–2.9 times higher than those in outdoor ambient, respectively. Numerical simulation indicated that the predicted concentrations were underestimated by a factor of 8% in comparison with the measured ones.     

Statistical evaluation of PM<Subscript>10</Subscript> and distribution of PM<Subscript>1</Subscript>, PM<Subscript>2.5</Subscript>, and PM<Subscript>10</Subscript> in ambient air due to extreme fireworks episodes (Deepawali festivals) in megacity Delhi

Temporal variation of PM₁₀ using 2-year data (January, 2007–December, 2008) of Delhi is presented. PM₁₀ varied from 42 to 200 μg m⁻³ over January to December, with an average 114.1 ± 81.1 μg m⁻³. They are comparable with the data collected by Central Pollution Control Board (National Agency which monitors data over the entire country in India) and are lower than National Ambient Air Quality (NAAQ) standard during monsoon, close to NAAQ during summer but higher in winter. Among CO, NO₂, SO₂, rainfall, temperature, and wind speed, PM₁₀ shows good correlation with CO. Also, PM₁₀, PM_2.5, and PM₁ levels on Deepawali days when fireworks were displayed are presented. In these festive days, PM₁₀, PM_2.5, and PM₁ levels were 723, 588, and 536 μg m⁻³ in 2007 and 501, 389, and 346 μg m⁻³ in 2008. PM₁₀, PM_2.5, and PM₁ levels in 2008 were 1.5 times lower than those in 2007 probably due to higher mixing height (446 m), temperature (23.8°C), and winds (0.36 ms⁻¹).     

Changes in PM<Subscript>10</Subscript> concentration due to large-scale rainfall

This paper is concerned with the estimation of the removal efficiency of PM₁₀ by large-scale precipitation under no-wind conditions in a background (rural) and urban areas. The changes in PM₁₀ concentrations before, during and after the presence of rainfall were studied from 2007 to 2013. The study was conducted in two different locations identified with regard to air quality. DAVIS weather stations were used to determine the meteorological conditions. The concentration of PM₁₀ was calculated with the use of the gravimetric reference method. Two hundred and ninety-nine measurement series were carried out. A linear relationship was found between the intensity and duration of rainfall and the value of the removal coefficient (ΔC). It was proved that except light rains, for the near-to-ground troposphere, the effectiveness of the removal of PM₁₀ (ΔC) did not assume different values at various locations for rainfall with the same intensity and duration. It was found that a temporary interaction of the effect of the purification by wet deposition was being minimised in areas characterised by low air quality. It was confirmed that intense rains resulted in the maintenance of higher values of air quality.     

Influences of air CO<Subscript>2</Subscript> on hydrochemistry of drip water and implications for paleoclimate study in a stream-developed cave,SW China

Cave air CO₂ is a vital part of the cave environment. Most studies about cave air CO₂ variations are performed in caves with no streams; there are few studies to date regarding the relationship of cave air CO₂ variations and drip water hydrochemistry in underground stream–developed caves. To study the relationship of underground stream, drip water, and cave air CO₂, monthly and daily monitoring of air CO₂ and of underground stream and drip water was performed in Xueyu Cave from 2012 to 2013. The results revealed that there was marked seasonal variation of air CO₂ and stream hydrochemistry in the cave. Daily variations of cave air CO₂, and of stream and drip water hydrochemistry, were notable during continuous monitoring. A dilution effect was observed by analyzing hydrochemical variations in underground stream and drip water after rainfall. High cave air CO₂ along with low pH and low δ¹³C_DIC in stream and drip water indicated that air CO₂ was one of the dominant factors controlling stream and drip water hydrochemistry on a daily scale. On a seasonal scale, stream flows may promote increased cave air CO₂ in summer; in turn, the higher cave air CO₂ could inhibit degassing of drip water and make calcite δ¹³C more negative. Variation of calcite δ¹³C (precipitated from drip water) was in reverse of monthly temperature, soil CO₂, and cave air CO₂. Therefore, calcite δ¹³C in Xueyu Cave could be used to determine monthly changes outside the cave. However, considering the different precipitation rate of sediment in different seasons, it was difficult to use stalagmites to reconstruct environmental change on a seasonal scale.     

Dynamics of CO<Subscript>2</Subscript> fluxes and concentrations during a shallow subsurface CO<Subscript>2</Subscript> release

A field facility located in Bozeman, Montana provides the opportunity to test methods to detect, locate, and quantify potential CO₂ leakage from geologic storage sites. From 9 July to 7 August 2008, 0.3 t CO₂ day⁻¹ were injected from a 100-m long, ~2.5-m deep horizontal well. Repeated measurements of soil CO₂ fluxes on a grid characterized the spatio-temporal evolution of the surface leakage signal and quantified the surface leakage rate. Infrared CO₂ concentration sensors installed in the soil at 30 cm depth at 0–10 m from the well and at 4 cm above the ground at 0 and 5 m from the well recorded surface breakthrough of CO₂ leakage and migration of CO₂ leakage through the soil. Temporal variations in CO₂ concentrations were correlated with atmospheric and soil temperature, wind speed, atmospheric pressure, rainfall, and CO₂ injection rate.     

Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013–2017

Based on long-term PM_2.5 data observed at high temporal and spatial resolution, the relationships between PM_2.5, primary emission, and weather factors in China during four seasons were examined using statistical analysis. The results reveal that primary emission plays a decisive role in the spatial distribution and seasonal variability of PM_2.5, except in western China, where PM_2.5 is controlled by dust weather. In addition to the accumulation of primary emissions, unfavorable meteorological conditions for the diffusion of air pollution lead to the occurrence of PM_2.5 pollution. The significant dynamic factors affecting PM_2.5 concentration are surface wind speed, planet boundary layer height, and ventilation coefficient, especially in winter. The ventilation coefficient is inversely correlated with PM_2.5. Better ventilation is more favorable for the dilution and outflow of local PM_2.5. However, in spring and autumn, ventilation coefficient and PM_2.5 are positively correlated over the southern regions with low emission, indicating that ventilation also affects the inflow of PM_2.5 from outside the region. Wind shear, 850 hPa divergence, and vertical velocity have insignificant effects on the long-term variations in PM_2.5. The significant thermal factor is 850 hPa temperature in winter, except in the Pearl River Delta and Xinjiang regions. In spring, the influence of each thermal factor is weak. In summer, the influences of temperature and humidity are more significant than in spring. In autumn, the influence of humidity is relatively obvious, compared with other thermal factors. The correlation coefficients between multi-factors regressed and observed PM_2.5 concentrations pass the 95% confidence test, and are higher than that of single-factor regression over most regions. The observed data from December 2016 to February 2017 were chosen to test the regression equation. The test result reveals that the regression equation is effective for predicting PM_2.5 concentrations over regions with high primary emission.     

Variations in dust-related PM<Subscript>10</Subscript> emission from an arid land due to surface composition and topsoil disturbance

Aeolian (wind) erosion is most common in arid regions. The resulted emission of PM₁₀ (particulate matter that is smaller than 10 μm in diameter) from the soil has many environmental and socioeconomic consequences such as soil degradation and air pollution. Topsoil resistance to aeolian transport highly depends on the surface composition. The study aim was to examine variations in PM₁₀ fluxes in a desert-dust source due to surface composition and topsoil disturbance. Aeolian field experiments using a boundary layer wind tunnel alongside soil composition analysis were integrated in this study. The results show variations in PM₁₀ fluxes (ranging from 9.5 to 524.6 mg m^?2 min^?1) in the studied area. Higher wind velocity increased significantly the PM₁₀ fluxes in all surface compositions. A short-term natural disturbance caused changes in the aggregate soil distribution (ASD) and increased significantly PM₁₀ emissions. Considering that PM₁₀ contains clays, organic matter, and absorbed elements, the recorded PM₁₀ fluxes are indicative of the potential soil loss and degradation by wind erosion in such resource-limited ecosystems. The findings have implications in modeling dust emission from a source area with complex surfaces.     

CO<Subscript>2</Subscript>-assisted removal of nutrients from municipal wastewater by microalgae <Emphasis Type="Italic">Chlorella vulgaris</Emphasis> and <Emphasis Type="Italic">Scenedesmus obliquus</Emphasis>

Axenic culture of microalgae Chlorella vulgaris ATCC^® 13482 and Scenedesmus obliquus FACHB 417 was used for phycoremediation of primary municipal wastewater. The main aim of this study was to measure the effects of normal air and CO₂-augmented air on the removal efficacy of nutrients (ammonia N and phosphate P) from municipal wastewater by the two microalgae. Batch experiments were carried out in cylindrical glass bottles of 1 L working volume at 25 °C and cool fluorescent light of 6500 lux maintaining 14/10 h of light/dark cycle with normal air supplied at 0.2 L min^?1 per liter of the liquid for both algal strains for the experimental period. In the next set of experiments, the treatment process was enhanced by using 1, 2 and 5% CO₂/air (vol./vol.) supply into microalgal cultures. The enrichment of inlet air with CO₂ was found to be beneficial. The maximum removal of 76.3 and 76% COD, 94.2 and 92.6% ammonia, and 94.8 and 93.1% phosphate after a period of 10 days was reported for C. vulgaris and S. obliquus, respectively, with 5% CO₂/air supply. Comparing the two microalgae, maximum removal rates of ammonia and phosphate by C. vulgaris were 4.12 and 1.75 mg L^?1 day^?1, respectively, at 5% CO₂/air supply. From kinetic study data, it was found that the specific rates of phosphate utilization (q _phsophate) by C. vulgaris and S. obliquus at 5% CO₂/air supply were 1.98 and 2.11 day^?1, respectively. Scale-up estimation of a reactor removing phosphate (the criteria pollutant) from 50 MLD wastewater influent was also done.     

Spatio-temporal variations of respirable particles at residential areas located in the vicinity of opencast coal projects,India: a case study

The objective of the study is to investigate spatio-temporal variations of PM₁₀, PM_2.5, and PM₁ concentrations at seven residential sites, located in the vicinity of opencast coal projects, Basundhara Garjanbahal Area (BGA), India. Meteorological parameters such as wind speed, wind direction, relative humidity, and temperature were collected simultaneously with PM concentrations. Mean concentrations of PM₁₀ in the range 215 ± 169–526 ± 412 μg m^?3, PM_2.5 in the range of 91 ± 79–297 ± 107 μg m^?3, PM₁ in the range of 68 ± 60–247 ± 84 μg m^?3 were obtained. Coarse fractions (PM_2.5–10) varied from 27 to 58% whereas fine fractions (PM_1–2.5 and PM₁) varied in the range of 51–73%. PM_2.5 concentration was 41–74% of PM₁₀ concentration, PM₁ concentration was 31–62% of PM₁₀ concentration, and PM₁ concentration was 73–83% of PM_2.5 concentration. Role of meteorology on PM concentrations was assessed using correlation analysis. Linear relationships were established among PM concentrations using least square regression analysis. With the aid of principal component analysis, two components were drawn out of eight variables, which represent more than 75% of variance. The results indicated that major sources of air pollutants (PM₁₀, PM_2.5, PM₁, CO, CO₂) at the residential sites are road dust raised by vehicular movement, spillage of coal generated during transportation, spontaneous combustion of coal, and biomass burning in village area.     

Diel Aquatic CO<Subscript>2</Subscript> System Dynamics of a Bermudian Mangrove Environment

Mangrove ecosystems play an important, but understudied, role in the cycling of carbon in tropical and subtropical coastal ocean environments. In the present study, we examined the diel dynamics of seawater carbon dioxide (CO₂) and dissolved oxygen (DO) for a mangrove-dominated marine ecosystem (Mangrove Bay) and an adjacent intracoastal waterway (Ferry Reach) on the island of Bermuda. Spatial and temporal trends in seawater carbonate chemistry and associated variables were assessed from direct measurements of dissolved inorganic carbon, total alkalinity, dissolved oxygen (DO), temperature, and salinity. Diel pCO₂ variability was interpolated across hourly wind speed measurements to determine variability in daily CO₂ fluxes for the month of October 2007 in Bermuda. From these observations, we estimated rates of net sea to air CO₂ exchange for these two coastal ecosystems at 59.8 ± 17.3 in Mangrove Bay and 5.5 ± 1.3 mmol m⁻² d⁻¹ in Ferry Reach. These results highlight the potential for large differences in carbonate system functioning and sea-air CO₂ flux in adjacent coastal environments. In addition, observation of large diel variability in CO₂ system parameters (e.g., mean pCO₂: 390–2,841 μatm; mean pH_T: 8.05–7.34) underscores the need for careful consideration of diel cycles in long-term sampling regimes and flux estimates.     

The influence of degradation of the swamp and alpine meadows on CH<Subscript>4</Subscript> and CO<Subscript>2</Subscript> fluxes on the Qinghai-Tibetan Plateau

CH₄ and CO₂ fluxes from a high-cold swamp meadow and an alpine meadow on the Qinghai-Tibetan Plateau, subject to different degrees of degradation, were measured over a 12-month period. Air temperature, soil temperature and moisture, and the depths of the water table and thawing-freezing layer were determined. For swamp meadows, the greater the degradation, the lesser the carbon efflux. CH₄ emissions at the nondegraded swamp meadow site were 1.09–3.5 and 2.5–11.27 times greater, and CO₂ emissions 1.08–1.69 and 1.41–4.43 times greater, respectively, than those from moderately and severely degraded sites. For alpine meadows, the greater the degradation, the greater the CH₄ consumption and CO₂ emissions. CH₄ consumption at the severely degraded alpine meadow site was 6.6–21 and 1.1–5.25 times greater, and CO₂ emissions 1.05–78.5 and 1.04–6.28 times greater, respectively, than those from the nondegraded and moderately degraded sites. The CH₄ and CO₂ fluxes at both sites were significantly correlated (R ² > 0.59, P < 0.05) with air temperature, soil temperature, and topsoil (0–5 cm depth) moisture, indicating these to be the main environmental factors affecting such fluxes.     

Bayesian hierarchical models for soil CO<Subscript>2</Subscript> flux and leak detection at geologic sequestration sites

Proper characterizations of background soil CO₂ respiration rates are critical for interpreting CO₂ leakage monitoring results at geologic sequestration sites. In this paper, a method is developed for determining temperature-dependent critical values of soil CO₂ flux for preliminary leak detection inference. The method is illustrated using surface CO₂ flux measurements obtained from the AmeriFlux network fit with alternative models for the soil CO₂ flux versus soil temperature relationship. The models are fit first to determine pooled parameter estimates across the sites, then using a Bayesian hierarchical method to obtain both global and site-specific parameter estimates. Model comparisons are made using the deviance information criterion (DIC), which considers both goodness of fit and model complexity. The hierarchical models consistently outperform the corresponding pooled models, demonstrating the need for site-specific data and estimates when determining relationships for background soil respiration. A hierarchical model that relates the square root of the CO₂ flux to a quadratic function of soil temperature is found to provide the best fit for the AmeriFlux sites among the models tested. This model also yields effective prediction intervals, consistent with the upper envelope of the flux data across the modeled sites and temperature ranges. Calculation of upper prediction intervals using the proposed method can provide a basis for setting critical values in CO₂ leak detection monitoring at sequestration sites.     

Lockdown during COVID-19 pandemic: A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PM_2.5 and NO₂ concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM_2.5 and NO₂ concentration in urban cities, causing low concentrations. It is observed that the original PM_2.5 and NO₂ concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM_2.5 and NO₂ concentrations. Hurst exponent of the PM_2.5 and NO₂ concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.     

Indoor and outdoor urban atmospheric CO2: Stable carbon isotope constraints on mixing and mass balance

From July to November 2009, concentrations of CO₂ in 78 samples of ambient air collected in 18 different interior spaces on a university campus in Dallas, Texas (USA) ranged from 386 to 1980 ppm. Corresponding δ¹³C values varied from −8.9‰ to −19.4‰. The CO₂ from 22 samples of outdoor air (also collected on campus) had a more limited range of concentrations from 385 to 447 ppm (avg. = 408 ppm), while δ¹³C values varied from −10.1‰ to −8.4‰ (avg.=-9.0‰). In contrast to ambient indoor and outdoor air, the concentrations of CO₂ exhaled by 38 different individuals ranged from 38,300 to 76,200 ppm (avg. = 55,100 ppm), while δ¹³C values ranged from −24.8‰ to −17.7‰ (avg. = −21.8‰). The residence times of the total air in the interior spaces of this study appear to have been on the order of 10 min with relatively rapid approaches (∼30 min) to steady-state concentrations of ambient CO₂ gas. Collectively, the δ¹³C values of the indoor CO₂ samples were linearly correlated with the reciprocal of CO₂ concentration, exhibiting an intercept of −21.8‰, with r² = 0.99 and p < 0.001 (n = 78). This high degree of linearity for CO₂ data representing 18 interior spaces (with varying numbers of occupants), and the coincidence of the intercept (−21.8‰) with the average δ¹³C value for human-exhaled CO₂ demonstrates simple mixing between two inputs: (1) outdoor CO₂ introduced to the interior spaces by ventilation systems, and (2) CO₂ exhaled by human occupants of those spaces. If such simple binary mixing is a common feature of interior spaces, it suggests that the intercept of a mixing line defined by two data points (CO₂ input from the local ventilation system and CO₂ in the ambient air of the room) could be a reasonable estimate of the average δ¹³C value of the CO₂ exhaled by the human occupants. Thus, such indoor spaces appear to constitute effective “sample vessels” for collection of CO₂ that can be used to determine the average proportions of C₃ and C₄-derived C in the diets of the occupants. For the various groups occupying the rooms sampled in this study, C₄-derived C appears to have constituted ∼40% of the average diet.     

Temporal analysis of air pollution and its relationship with meteorological parameters in Bahrain, 2006–2012

The objective of this paper is to analyze temporal and seasonal trends of air pollution in Bahrain between 2006 and 2012 by utilizing datasets from five air quality monitoring stations. The non-parametric and robust Theil-Sen approach is employed to study quantitatively temporal variations of particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃). The calculated annual concentrations for PM₁₀ and PM_2.5 in Bahrain were substantially higher than recommended World Health Organization (WHO) guideline standards. Results showed increasing trends for PM₁₀, PM_2.5, and SO₂ whereas O₃ and its precursor NO₂ showed decreasing behavior. The general increase in air pollution trends is in agreement with prediction of air pollution models for Middle East region due to economic growth, industrialization, and urbanization. The significances of long-term trends were examined. Additional to actual (unadjusted) trends, meteorological adjusted (deseasonalized) trends and seasonal trends were quantified. The box-plot analysis visually illustrated monthly variations of key air pollutants. It showed that only PM₁₀ and PM_2.5 exhibited seasonal pattern, and their concentrations increased during summer and decreased during winter. The effects of ambient air temperature, relative humidity, wind speed, and rainfall on particulate matter (PM) concentrations were further investigated. The Spearman correlation coefficient results demonstrated significant negative correlation between relative humidity and PM concentrations (??0.595 for PM₁₀ and ??0.526 for PM_2.5) while significant positive correlation was observed between temperature and PM concentrations (0.420 for PM₁₀ and 0.482 for PM_2.5).     

Depth of volcanic basalt degassing forecasted from CO2 fluid inclusions

Fluid inclusions have recorded the history of degassing in basalt. Some fluid inclusions in olivine and pyroxene phenocrysts of basalt were analyzed by micro-thermometry and Raman spectroscopy in this paper. The experimental results showed that many inclusions are present almost in a pure CO2 system. The densities of some CO2 inclusions were computed in terms of Raman spectroscopic characteristics of CO2 Fermi resonance at room temperature. Their densities change over a wide range, but mainly between 0.044 g/cm3 and 0.289 g/cm3. Their micro-thermometric measurements showed that the CO2 inclusions examined reached homogenization between 1145.5℃ and 1265℃ . The mean value of homogenization temperatures of CO2 inclusions in basalts is near 1210℃. The trap pressures (depths) of inclusions were computed with the equation of state and computer program. Distribution of the trap depths makes it know that the degassing of magma can happen over a wide pressure (depth) range, but mainly at the depth of 0.48 km to 3.85 km. This implicates that basalt magma experienced intensive degassing and the CO2 gas reservoir from the basalt magma also may be formed in this range of depths. The results of this study showed that the depth of basalt magma degassing can be forecasted from CO2 fluid inclusions, and it is meaningful for understanding the process of magma degassing and constraining the inorganogenic CO2 gas reservoir.     

Carbon dioxide in Postojna Cave (Slovenia): spatial distribution,seasonal dynamics and evaluation of plausible sources and sinks

The CO₂ concentration of the air in Postojna Cave (400–7900 ppm) is found to be induced by CO₂ sources (human respiration contributing?~?20,000–58,000 ppm per breath, outgassing of dripwater and water seeping from the vadose zone/epikarst with a pCO₂ values of 5000–29,000 ppm, and underground Pivka River having pCO₂ at 2344–4266 ppm) and CO₂ dilution (inflow of outside air with a CO₂ concentration of?~?400 ppm). Measurements show that sinking Pivka River has the lowest CO₂ concentration among plausible CO₂ sources but still continuously exceeds the surrounding cave air CO₂ concentration. During the winter months, intensive ventilation reduces the cave air CO₂ concentration to outside levels (~?400 ppm), even in the centre of the cave system. CO₂ dilution is less pronounced in summer (CO_2(min)?≈?800 ppm), since the ventilation rate is not as strong as in winter and the outside air that enters the cave through breathing holes and fractures is enriched with soil CO₂. During spring and autumn, the daily alternation of the ventilation regime with a smaller rate of air exchange results in yearly cave air CO₂ peaks of up to?~?2400 ppm. Some dead-end passages can be much less affected by ventilation, resulting in a cave air CO₂ concentration of up to 7900 ppm. The strongest diurnal CO₂ peaks due to human respiration were recorded during the spring holidays (increase of up to 1300 ppm day^?1), compared to considerably smaller summer peaks despite peak visits (increase of?~?600 ppm day^?1).     

Energy and CO<Subscript>2</Subscript> exchanges and influencing factors in spring wheat ecosystem along the Heihe River,northwestern China

Spring wheat (Triticum aestivum Linn.) is an important crop for food security in the desert-oasis farmland in the middle reaches of the Heihe River in northwestern China. We measured fluxes using eddy covariance and meteorological parameters to explore the energy fluxes and the relationship between CO₂ flux and climate change in this region during the wheat growing seasons in 2013 and 2014. The energy balance closures were 70.5% and 72.7% in the 2013 and 2014 growing season, respectively. The wheat ecosystem had distinct seasonal and diurnal dynamics of CO₂ fluxes with U-shaped curves. The accumulated net ecosystemic CO₂ exchanges (NEE) were -111.6 and -142.2 g C/m² in 2013 and 2014 growing season, respectively. The ecosystem generally acted as a CO₂ sink during the growing season but became a CO₂ source after the wheat harvest. A correlation analysis indicated that night-time CO₂ fluxes were exponentially dependent on air temperature and soil temperature at a depth of 5 cm but were not correlated with soil-water content, water-vapour pressure, or vapour-pressure deficit. CO₂ flux was not correlated with the meteorological parameters during daytime. However, irrigation and precipitation, may complicate the response of CO₂ fluxes to other meteorological parameters.