scholarly journals Measurement report: Short-term variation of ammonia concentration in an urban area: contributions of mist evaporation and emissions from a forest canopy with bird droppings

2020 ◽  
Author(s):  
Kazuo Osada
Keyword(s):  
Urban Area ◽  
Air Temperature ◽  
Forest Canopy ◽  
Leaf Growth ◽  
Anthropogenic Sources ◽  
Daily Maximum ◽  
Short Term ◽  
Daily Minimum ◽  
Bird Droppings ◽  
Morning Peak

Abstract. Short-term variations of NH3 concentrations in the urban atmosphere are affected by local meteorological conditions and variations of natural and anthropogenic sources. To investigate potential sources and processes of NH3 variation in an urban area, hourly NH3 and NH4+ concentrations were measured from November 2017 through October 2019 in Nagoya, a megacity located in central Japan. Monthly averages of NH3 concentrations were high in summer and low in winter. Daily minimum NH3 concentrations were almost linearly correlated with daily minimum air temperature. In contrast, daily maximum NH3 concentrations revealed an exponential increase with temperature, suggesting that different processes with air temperature acted during the nighttime and daytime. Short-term increases of NH3 concentrations of two types were examined closely. The first is a rare but large increase (11 ppb for 2 hr) after mist evaporation during daytime. It is noteworthy that an event of this magnitude was identified only once during two years of observations at Nagoya even though evaporation of mist or fog droplets is expected to be frequent after rain. The second short-term increase was a large morning peak in summer. After selected days were fulfilled with non-wet and weak wind conditions, the amplitude of diurnal variation of NH3 concentration (daily maximum minus minimum) was analyzed: the amplitude was small (ca. 2 ppb) in winter but it increased from early summer along with new leaf growth. It peaked in summer (up to ca. 20 ppb) during intense addition of droppings from hundreds of crows on trees in the campus assembled before roosting. The high daily maximum NH3 concentration was characterized by a rapid increase occurring 2–4 hr after local sunrise. Daily and seasonal findings related to the morning peak implied that stomatal emission at the site was responsible for the increase. The yearly difference between daily amplitudes during the two summers was explained by the difference in the input amounts of reactive nitrogen derived from bird droppings and some rain, suggesting that the canopy of a small forest affected by the bird droppings might act as a temporary but strong source of NH3.

scholarly journals Measurement report: Short-term variation in ammonia concentrations in an urban area increased by mist evaporation and emissions from a forest canopy with bird droppings

2020 ◽  
Vol 20 (20) ◽  
pp. 11941-11954
Author(s):  
Kazuo Osada
Keyword(s):  
Urban Areas ◽  
Forest Canopy ◽  
Anthropogenic Sources ◽  
Daily Maximum ◽  
Short Term ◽  
Daily Minimum ◽  
Bird Droppings ◽  
Morning Peak ◽  
Air Temperatures ◽  
Cloudy Weather

Abstract. Local meteorological conditions and natural and anthropogenic sources affect atmospheric NH3 concentrations in urban areas. To investigate potential sources and processes of NH3 variation in urban areas, hourly NH3 and NH4+ concentrations were measured during November 2017–October 2019 in Nagoya, a central Japanese megacity. Average NH3 concentrations are high in summer and low in winter. Daily minimum NH3 concentrations are linearly correlated with daily minimum air temperatures. By contrast, daily maximum NH3 concentrations increase exponentially with temperature, suggesting that different nighttime and daytime processes and air temperatures affect concentrations. Short-term increases in NH3 concentrations of two types were examined closely. Infrequent but large increases (11 parts per billion (ppb) for 2 h) occurred after mist evaporation during daytime. During 2 years of observations, only one event of this magnitude was identified in Nagoya, although evaporation of mist and fog occurs frequently after rains. Also, short-term increases occur with a large morning peak in summer. Amplitudes of diurnal variation in NH3 concentration (daily maximum minus minimum) were analyzed on days with nonwet and low wind conditions. Amplitudes were small (ca. 2 ppb) in winter, but they increased from early summer along with new leaf growth. Amplitudes peaked in summer (ca. 20 ppb) because of droppings from hundreds of crows before roosting in trees on the campus. High daily maximum NH3 concentrations were characterized by a rapid increase occurring 2–4 h after local sunrise. In summer, peak NH3 concentrations at around 08:00 local time (LT) in sunny weather were greater than in cloudy weather, suggesting that direct sunlight particularly boosts the morning peak. Daily and seasonal findings related to the morning peak imply that stomatal emission at the site causes the increase. Differences between daily amplitudes during the two summers was explained by the different input amounts of reactive nitrogen from bird droppings and rain, suggesting that bird droppings, a temporary rich source of NH3, affected the small forest canopy.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

scholarly journals Climatic change on King George Island in the years 1948–2011

2013 ◽  
Vol 34 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Marek Kejna ◽  
Andrzej Araźny ◽  
Ireneusz Sobota
Keyword(s):  
Climatic Change ◽  
Air Temperature ◽  
Atmospheric Precipitation ◽  
Warming Trend ◽  
Air Pressure ◽  
South Shetland Islands ◽  
Daily Maximum ◽  
King George Island ◽  
Daily Minimum ◽  
Winter Time

Abstract The climatic change on King George Island (KGI) in the South Shetland Islands, Antarctica, in the years of 1948-2011 are presented. In the reference period, a statistically significant increase in the air temperature (0.19ºC/10 years, 1.2ºC in the analysed period) occurred along with a decrease in atmospheric pressure (−0.36 hPa/10 years, 2.3 hPa). In winter time, the warming up is more than twice as large as in summer. This leads to decrease in the amplitude of the annual cycle of air temperature. On KGI, there is also a warming trend of daily maximum and daily minimum air temperature. The evidently faster increase in daily minimum results in a decrease of the diurnal temperature range. The largest changes of air pressure took place in the summertime (−0.58 hPa/10 years) and winter (−0.34 hPa/10 years). The Semiannual Oscillation pattern of air pressure was disturbed. Climate changes on KGI are correlated with changing surface temperatures of the ocean and the concentra− tion of sea ice. The precipitation on KGI is characterised by substantial variability year to year. In the analysed period, no statistically significant trend in atmospheric precipitation can be observed. The climate change on KGI results in substantial and rapid changes in the environment, which poses a great threat to the local ecosystem.

Habitat fragmentation causes collapse of kelp recruitment

2020 ◽  
Vol 648 ◽  
pp. 111-123
Author(s):  
C Layton ◽  
MJ Cameron ◽  
M Tatsumi ◽  
V Shelamoff ◽  
JT Wright ◽  
...  
Keyword(s):  
Habitat Fragmentation ◽  
Patch Size ◽  
Life Stage ◽  
Extended Period ◽  
Anthropogenic Disturbances ◽  
Anthropogenic Sources ◽  
Kelp Forests ◽  
Natural Disturbances ◽  
Short Term ◽  
Turf Algae

Kelp forests in many regions are experiencing disturbance from anthropogenic sources such as ocean warming, pollution, and overgrazing. Unlike natural disturbances such as storms, anthropogenic disturbances often manifest as press perturbations that cause persistent alterations to the environment. One consequence is that some kelp forests are becoming increasingly sparse and fragmented. We manipulated patch size of the kelp Ecklonia radiata over 24 mo to simulate persistent habitat fragmentation and assessed how this influenced the demography of macro- and microscopic juvenile kelp within the patches. At the beginning of the experiment, patch formation resulted in short-term increases in E. radiata recruitment in patches <1 m2. However, recruitment collapsed in those same patches over the extended period, with no recruits observed after 15 mo. Experimental transplants of microscopic and macroscopic juvenile sporophytes into the patches failed to identify the life stage impacted by the reductions in patch size, indicating that the effects may be subtle and require extended periods to manifest, and/or that another life stage is responsible. Abiotic measurements within the patches indicated that kelp were less able to engineer the sub-canopy environment in smaller patches. In particular, reduced shading of the sub-canopy in smaller patches was associated with proliferation of sediments and turf algae, which potentially contributed to the collapse of recruitment. We demonstrate the consequences of short- and longer-term degradation of E. radiata habitats and conclude that habitat fragmentation can lead to severe disruptions to kelp demography.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

Micro-climatological influences on temperature condition in an old people’s home in Helsinki, Finland, caused by extended heat-waves 

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä
Keyword(s):  
Air Temperature ◽  
Heat Waves ◽  
Daily Maximum ◽  
Old People ◽  
Concrete Walls ◽  
Meteorological Forcing ◽  
Clear Sky ◽  
Wave Event ◽  
Construction Type ◽  
Maximum Air Temperature

&lt;p&gt;In our research we describe the micro-climatological influences of two heat-waves around and the air temperature development in a certain old people&amp;#8217;s home in Helsinki, Finland. The stand-alone six-storey concrete building was erected in the late 1970&amp;#8217;s and represents the prevailing construction type of this area. The building is located on a slightly southwards declining slope.&lt;/p&gt;&lt;p&gt;The first simulation used real meteorological forcing-data from the heat-wave event in summer 2018, which lasted from July, 13&lt;sup&gt;th&lt;/sup&gt; until August, 5&lt;sup&gt;th&lt;/sup&gt;. In this period the daily maximum air temperature reached almost every day 25 &amp;#176;C and more, sometimes even more than 30 &amp;#176;C. All air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptical weather station.&lt;/p&gt;&lt;p&gt;The second simulation used fourteen-day constructed meteorological forcing-data, based on a clear-sky, slowly increasing air temperature, higher than normal humidity, and low wind conditions assumption starting on July, 13&lt;sup&gt;th&lt;/sup&gt; (day 194 of the year).&lt;/p&gt;&lt;p&gt;We used the holistic ENVI-met simulation soft-ware to simulate the physical environment around the old people&amp;#8217;s home and especially the energy fluxes inside the concrete walls to explain the needs for cooling demands.&lt;/p&gt;&lt;p&gt;The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).&lt;/p&gt;

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