scholarly journals Short-Term Aerial Pollutant Concentrations in a Southwestern China Pig-Fattening House

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 103
Author(s):  
Shihua Pu ◽  
Xiao Rong ◽  
Jiaming Zhu ◽  
Yaqiong Zeng ◽  
Jian Yue ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Suspended Particle ◽  
Southwestern China ◽  
Indoor Temperature ◽  
Ventilation Control ◽  
Temperature Correlation ◽  
Pollutant Concentrations ◽  
Seasonal And Diurnal Variations

Concentrations of critical aerial pollutants within animal farms are important to the health of animals and farm staff and can be reduced via manure management, ventilation control, and barn design. This study characterized measurements of ammonia (NH3), total suspended particle (TSP), and airborne microbial communities of a large-scale pig-fattening house, as well as their correlations with environmental variables in Southwestern China. Monitoring was conducted for 15 consecutive days during both August and January, at various locations inside the pig house. The concentrations of NH3 and TSP averaged 3.22 and 0.55 mg m−3, respectively, while the average number of airborne microbial colonies was 3.91 log cfu m−3. The aerial pollutant concentrations displayed significant seasonal differences (p < 0.05). Specifically, concentrations in winter were significantly higher than those in summer (p < 0.05), and the 07:00 measurements were the highest among the three measurement times. The concentrations were significantly correlated with indoor temperature and relative humidity. In summer, TSP concentration was negatively correlated with temperature (correlation coefficient = −0.732), while NH3 concentration was positively correlated with temperature (correlation coefficient = 0.58). In winter, TSP and NH3 concentrations were negatively correlated with relative humidity (correlation coefficients = −0.739 and −0.713, respectively), while the airborne microbial colonies were not correlated with either humidity or temperature in summer or winter. These findings confirm that the aerial pollutant concentrations in a Southwestern China pig-fattening house exhibited significant seasonal and diurnal variations. Air quality can be improved by more precise ventilation control as observed by the correlation of concentrations with ventilation control, indoor temperature, and humidity.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

Cadmium exposure as a key risk factor for residents in a world large-scale barite mining district, southwestern China

Chemosphere ◽  
2021 ◽  
Vol 269 ◽  
pp. 129387
Author(s):  
Qinhui Lu ◽  
Zhidong Xu ◽  
Xiaohang Xu ◽  
Lin Liu ◽  
Longchao Liang ◽  
...  
Keyword(s):  
Risk Factor ◽  
Large Scale ◽  
Cadmium Exposure ◽  
Southwestern China ◽  
Mining District

scholarly journals Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models

2006 ◽  
Vol 19 (17) ◽  
pp. 4344-4359 ◽  
Author(s):  
Markus Stowasser ◽  
Kevin Hamilton
Keyword(s):  
Relative Humidity ◽  
Vertical Velocity ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Grid Point ◽  
Global Climate Models ◽  
Coupled Models ◽  
Cloud Radiative Forcing ◽  
Cloud Forcing

Abstract The relations between local monthly mean shortwave cloud radiative forcing and aspects of the resolved-scale meteorological fields are investigated in hindcast simulations performed with 12 of the global coupled models included in the model intercomparison conducted as part of the preparation for Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In particular, the connection of the cloud forcing over tropical and subtropical ocean areas with resolved midtropospheric vertical velocity and with lower-level relative humidity are investigated and compared among the models. The model results are also compared with observational determinations of the same relationships using satellite data for the cloud forcing and global reanalysis products for the vertical velocity and humidity fields. In the analysis the geographical variability in the long-term mean among all grid points and the interannual variability of the monthly mean at each grid point are considered separately. The shortwave cloud radiative feedback (SWCRF) plays a crucial role in determining the predicted response to large-scale climate forcing (such as from increased greenhouse gas concentrations), and it is thus important to test how the cloud representations in current climate models respond to unforced variability. Overall there is considerable variation among the results for the various models, and all models show some substantial differences from the comparable observed results. The most notable deficiency is a weak representation of the cloud radiative response to variations in vertical velocity in cases of strong ascending or strong descending motions. While the models generally perform better in regimes with only modest upward or downward motions, even in these regimes there is considerable variation among the models in the dependence of SWCRF on vertical velocity. The largest differences between models and observations when SWCRF values are stratified by relative humidity are found in either very moist or very dry regimes. Thus, the largest errors in the model simulations of cloud forcing are prone to be in the western Pacific warm pool area, which is characterized by very moist strong upward currents, and in the rather dry regions where the flow is dominated by descending mean motions.

Rising Planetary Boundary Layer Height over the Sahara Desert and Arabian Peninsula in a Warming Climate

2021 ◽  
Vol 34 (10) ◽  
pp. 4043-4068
Author(s):  
Liming Zhou ◽  
Yuhong Tian ◽  
Nan Wei ◽  
Shu-peng Ho ◽  
Jing Li
Keyword(s):  
Boundary Layer ◽  
Relative Humidity ◽  
Planetary Boundary Layer ◽  
Large Scale ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Statistical Significance ◽  
Maximum Height ◽  
Sahara Desert ◽  
Sensible Heat

AbstractTurbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth’s surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations.

scholarly journals Hypertension, pregnancy and weather: is seasonality involved?

2014 ◽  
Vol 60 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Brena Melo ◽  
Melania Amorim ◽  
Leila Katz ◽  
Isabela Coutinho ◽  
José Natal Figueiroa
Keyword(s):  
Relative Humidity ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Hypertensive Disorder ◽  
Air Humidity ◽  
Hypertensive Disorders Of Pregnancy ◽  
Monthly Temperature ◽  
Hypertensive Disorders ◽  
Relative Air Humidity ◽  
Hypertensive Disorder Of Pregnancy

Objective: The present study aimed at assessing the association between environmental temperature and the relative humidity of the air with frequency of hypertensive disorders of pregnancy. Methods: A prospective and retrospective, descriptive, ecological study was held at a teaching maternity in Recife, Brazil. Data from all 26.125 pregnant women admitted between 2000 and 2006 were analysed and 5.051 had the diagnosis of hypertensive disorder of pregnancy. The incidence percentages were calculated monthly per deliveries. Data on mean monthly temperature and relative humidity of the air were collected and monthly comparisons were conducted. February was chosen as the reference month due to its lowest incidence of the disease. The relative chance of hypertensive disorders of pregnancy for each other month was estimated by odds ratio and Pearson's correlation coefficient was used to calculate the relation between the incidence of hypertensive disorders of pregnancy and the mean monthly temperature and relative air humidity. Results: February presented the lowest mean monthly incidence (9.95%) and August the highest (21.54%). Pearson correlation coefficient revealed a higher incidence of hypertensive disorders of pregnancy in the cooler months (r= -0.26; p=0.046) and no significant effect of relative air humidity (r=0.20; p=0.128). Conclusion: The incidence of hypertensive disorders of pregnancy may be affected by variations in temperature, increasing during cooler periods.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

scholarly journals Cultivation of Milky Mushroom (Calocybeindica) in Greenhouse

2021 ◽  
pp. 63-69
Author(s):  
P. Indraja ◽  
M. Madhava ◽  
S. Satyam ◽  
P. R. Chandra ◽  
S. Joy Prince
Keyword(s):  
Relative Humidity ◽  
Traditional Method ◽  
Large Scale ◽  
Nutritive Value ◽  
Edible Mushroom ◽  
Atmospheric Temperature ◽  
The Other ◽  
Controlled Environment ◽  
Mushroom Cultivation ◽  
Experimental Conditions

Mushroom cultivation is one of the most important steps in diversification of agriculture. Milky mushroom (Calocybeindica) is a tropical edible mushroom, popular because of its good nutritive value and it can be cultivated commercially on large scale. Generally, by creating controlled environment in rooms mushroom cultivation is taken up, In traditional method  it is typical to manage the atmospheric temperature and humidity in desired range, which can be maintained easily in greenhouse by automatic control system.Cultivation of milky mushroom in greenhouse was taken up in controlled environment under three different conditions of controlled temperature and relative humidity (RH). The experimental conditions are 28°C and 79% RH, 24°C and 84% RH and 32°C and 72%RH. The yield obtained was found maximum at 24°C temperature and 84% relative humidity when compared with the other two greenhouse environment conditions. The yield from the mushroom cultivation under controlled environmental conditions in greenhouse was found to be high when compared with the conventional practice. It was concluded that, the mushroom cultivation yields high under controlled greenhouse conditions and also economical compared to the conventional method.

Abstract 19: Using the Heckman Selection Model to Assess Selection Bias in Ischemic Stroke Patients with Documented NIHSS

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Michael P Thompson ◽  
Zhehui Luo ◽  
Joseph Gardiner ◽  
James F Burke ◽  
Mathew J Reeves
Keyword(s):  
Ischemic Stroke ◽  
Correlation Coefficient ◽  
Selection Bias ◽  
Large Scale ◽  
Selection Process ◽  
Selection Model ◽  
Stroke Patients ◽  
Heckman Selection Model ◽  
Nihss Score ◽  
Heckman Selection

Objective: Complete documentation in large scale datasets such as administrative data or disease registries is often difficult. Given that the subset of patients with complete data documentation are most likely not a random sample of patients, selection bias threatens the validity of results if a complete case analysis is used. To demonstrate, we will assess the presence and magnitude of selection bias in ischemic stroke patients with documented National Institute of Health Stroke Scale (NIHSS) [[Unable to Display Character: &#8211;]] which is often incomplete [[Unable to Display Character: &#8211;]] using the Heckman Selection Model. Methods: Patient level variables including demographics, comorbidities, clinical EMS and admission variables, and medical history/comorbidities were obtained from 10,717 ischemic stroke patients aged 65 and older in the Michigan Stroke Registry in 2009-2012. The Heckman Selection Model assesses the presence and magnitude of selection bias by estimating a correlation coefficient between error components of a linear regression model predicting patient NIHSS score [[Unable to Display Character: &#8211;]] the outcome model [[Unable to Display Character: &#8211;]] and a binary probit model predicting NIHSS documentation [[Unable to Display Character: &#8211;]] the selection model [[Unable to Display Character: &#8211;]] conditional on patient and hospital predictors. The outcome model predicting NIHSS score was specified using a backward selection process with stepwise deletion of non-significant predictors. The selection model included all variables in the outcome model, plus additional significant predictors of NIHHS documentation. Quasi-maximum likelihood estimation was used to produce robust standard errors. All analyses were done using PROC QLIM procedure in SAS. Results: 7,956 cases (74.2%) of cases had NIHSS documented. Significant predictors in the outcome and selection models are shown in the Table. The Heckman Selection Model found a statistically significant but modest correlation coefficient of ρ =0.1089 (SE=0.0119, p<0.0001). The positive correlation indicates that NIHSS was more likely to be documented in patients with higher NIHSS scores, i.e., more severe strokes. Conclusions: We found statistically significant albeit weak selection bias in the documentation of NIHSS in stroke patients. The Heckman Selection Model is a novel method that can be used to assess the presence and magnitude of selection bias when missing data is common.

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