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The Role of Vegetation in Reducing Anthropogenic CO2 in Bogor City
Full text linkVegetation has a role in reducing CO2 from anthropogenic activities through photosynthesis. Fuel combustion is one of the activities that greatly contribute to CO2 emissions. As a city with many destinations, the possibility of CO2 emissions will increase in Bogor especially on holidays because of motorized vehicle from other cities. This research aims to determine the absorption capability of vegetation in Bogor City in reducing CO2 emitted from fuel combustion. We analyzed CO2 data for 2017 by day to obtain traffic levels in the city assuming that people mobility using vehicle was influenced by day. Then we separated CO2 data into slow and fast photosynthesis rate based on air temperature. We determined the absorption capability of vegetation at daily basis by calculating the difference between the min and the max of CO2 concentration divided by the min of CO2. Our results showed that the lowest CO2 level was in Sunday. On that day, the average air temperatur was high indicating the less CO2 concentration. Our one-way Anova test confirmed this finding. The finding revealed that the absorption capability of vegetation to reduce anthropogenic CO2 was still limited. To implement Bogor as green city, more vegetations and gardens are needed to balance an increased CO2
Spatial Distribution of Dryness on Oil Palm Plantations Using Landsat image
Full text linkPeatland in Riau is commonly utilized for agricultural purposes including oil palm. This utilization has influenced on peat characteristics on the top soil leading to degraded peatland, associated drought-related fire. In this paper, we identified peat dryness from three different oil palm ages using drought indices proxy to derive information on spatial dryness. Two drought indices were used in this study including the Temperature Vegetation Dryness Index (TVDI) and the Crop Water Stress Index (CWSI). Our results showed that the TVDI value ranged from 0.46 to 0.92, while the CWSI value ranged from 0.18 to 0.80. The highest value of TVDI was found in 2-years old oil palm, and the lowest values was in the 11-years old oil palm. Our CWSI analysis confirmed this pattern that young oil palm has a high moisture stress, as many peat-soils were exposed to direct sunlight. Our findings also revealed that the TVDI and the CWSI were able to interpret soil moisture dynamics on the top layers (10 cm)
Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs
Full text linkPredictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2
Ability of Ornamental Plants in Adsorbing Dust from Vehicles (Case Study: Bumi Serpong Damai)
Full text linkThis research measured several vegetations that were planted in Bumi Serpong Damai, South Tangerang to absorb dust pollutions produced by vehicles. The locations for monitoring were divided based on traffic levels: high, medium and low. We measured the pollution based on two approaches i.e. measuring every four hours and a daily measurement. Based on our monitoring, each species will have different feedbacks to dust pollution at various traffic conditions. We found that species of Heliconia was able to absorb the dust at the top for high traffic condition, whereas Kaca Piring is effective for medium traffic. Our findings revealed that monitoring dust should be frequent at least four hours/day, and selection of species for reducing dust pollution should consider the leave structure.
Micro Climate Humidity in Nursery and Production Various Varieties Melon (Cucumis melo L.) in PKHT Tajur II
No full textMicro-climatic conditions may affect the growth and productivity of different genotypes of melon farm. This study aims to assess the effect of different moisture conditions in the melon’s nursery to the growth and production of different melon’s genotypes. To observe the effect of moisture, we monitored agronomical (leaf-area index, plant height, fruit weight) and micro-meteorological (transpiration, radiation interception) parameters for two treatments i.e. without modification of moisture (control) and with modification of moisture for period August-November 2015 at the Experimental Garden of IPB in Tajur II-Bogor. Totally, twelve genotypes of melon were used in the study. We found that a transpiration rate was reduced under the control treatment. It appears that the humidity treatment has a greater effect on both measured parameters. The plant height during the germination phased was affected by the humidity treatment, which was confirmed by the two statistical tests (ANOVA and t-test). In addition, our results showed that the treatment had influenced the harvesting time. Under the control treatment, melon seems to have a shorter time to harvest (about 61-63 days after planting), but a lower fruit weight. On the other hand, the modified humidity resulted in a longer time to harvest (68-71 days after planting) and a higher fruit weight. Further, with the treatment we found some genotypes that were potentially able to produce high yield, and some genotypes that were more resistant to dry conditions but they produced a relatively high yield
The Cooling Effect Estimation of Green Space Area Using an Empirical Approach in IPB Darmaga Campus
Full text linkGreen space area has contributed to increase atmospheric condition in surrounding area. Here we would like to test the cooling effect of small green area located in IPB campus Darmaga. We monitored air temperature at the morning (6 am) and afternoon (2 pm) for period March-June 2013 in three different sites in campus. Totally, we collected 658 observed data. Our results showed that partial shade area (PSA) and site were the most influenced factors that contributed to the cooling effect. It appears that the cooling effect was found until a distance of 50 m from each monitoring site. The cooling effect varied among sites, but it is consistent that the maximum effect occurred during afternoon. Our analysis confirmed that PSA has contributed to the cooling effect until 28%. Other factors that contributed to the cooling effect were vegetation characteristics and geometric configuration of the canopy. Further, our findings revealed that greenspace area is valuable to minimize high temperature effect from traffic street
Incidence Analysis of an Acute Respiratory Infection due to Climate Conditions and PM10 Concentration in West Jakarta Region
Full text linkHumans have contributed to an increased of particulates concentration due to their daily life activities including from transportation, industry, infrastructure and household. One common particulate found is PM10, which affects human health such as respiratory tract disorders. Weather condition controls PM10 concentration. This research aims to analyze the weather impact on PM10 concentration associated with the occurrence of acute respiratory infections. We analysed relationship between rainfall and PM10 on day to seasonal timescale resolution. Our results show a negative correlation between rainfall and PM10. It appears that season strongly influences the correlation with high and low PM10 concentration occurred during July-August (dry season) and December-February (wet season), respectively. At daily basis, our findings revealed that minimum PM10 concentration occurred at 06.00 am, and it will increase following human activities while people are going to workplace and school. Further, we found that a combining of low humidity and high PM 10 concentration will lead to high acute respiratory infections
Rainfall Prediction Using Artificial Neural Network
Full text linkArtificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R2 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction
Analysis of Climate Index with Historical Burn Analysis Method for Climate Change Adaptation (A Case Study in Pacitan District, East Java)
Full text linkDrought recurrently occurs in Indonesia, and it is one of the climate-related hazards that has a major impact on agriculture and food security. However, there is no a scheme, which allows any damages in agriculture associated with drought event will get an insurance. This study aims to analyze the climate index based on the potency of drought in Pacitan District, East Java to support the development of climate index insurance as an effort to climate change adaptation. This study used a climate index derived from monthly rainfall data, which was calculated based on the historical burn analysis (HBA) method. We examined climate index and measured exit value as representing of the lowest value which payment of insurance should be fully paid. Our results showed that the value varies among sub-districts in Pacitan. Kebonagung sub-district revealed the highest exit value (89 mm), which means the insurance company should pay the full insurance coverage if the rainfall in the period insured below 89 mm. The lowest exit value (18 mm) was in Pringkuku sub-district. Our finding revealed that the index HBA is suitable to be applied in regions with limited climate data. Furthermore, our approach could be one of the strategies to cope with drought to stabilize rice production during the dry season. For wide implementation, supports from government through regulation is needed
The Estimation of Rainwater Acidity Level based on the Ambient Air Pollutants Concentration (Case Study: DKI Jakarta)
Full text linkNowadays, acid rain is a common phenomenon occurring in metropolitan city, such as Jakarta. Human activities including transportation and industries in and surrounding this city have increased pollutants in the atmosphere, which lead to an increased of acid rain events. Analyzing on rainwater pH is common approach to assess whether an acid rain occurs or not. However, information on this pH value for greater Jakarta is limited. Here we used a combined of Henry\u27s law approach and Weather Research Forecasting-Chemistry (WRF-Chem) to estimate rainwater pH in Jakarta. The WRF-Chem was employed to generate SO2 and NO2 concentrations. Results showed that rainwater pH is below the threshold (pH = 5.6) in observation and modeling (Henry’s approach) throughout greater Jakarta. Rainwater pH showed a diurnal fluctuation with low value during night and morning, but high value at afternoon. Likely, season contributed to distribution of acid rain. Based on Henry’s approach, some regions (Bundaran HI, Kebon Jeruk, and Jagakarsa) revealed a high potency of acid rain for rainy season as indicated by the H+ concentration. On other hand, a high potency of acid rain during dry season was observed in Kelapa Gading and Bundaran HI. Our findings indicated that traffic may influence on rain acid events as shown by a high H+ concentration in Bundaran HI both dry and wet seasons