Judul: Identification of Ozone Long Range Transport using WRF Chem Model (Case Study : Bogor Area in 2018)
Abstrak: Ozon merupakan polutan sekunder yang terbentuk dari reaksi kimia antara prekursor (NO dan NO2) dengan radiasi matahari. Konsentrasi prekursor (NO dan NO2) yang tinggi dapat berkaitan dengan pembentukan ozon yang lebih intensif. Ozon dapat mengalami dispersi ke wilayah lain dengan jarak yang jauh atau dikenal dengan long range transport. Fenomena long range transport dapat dipengaruhi oleh angin. Tujuan penelitian ini adalah menganalisis karakteristik meteorologi serta konsentrasi ozon dan NOx, dan menganalisis fenomena long range transport ozon di wilayah Bogor. Data observasi yang digunakan yaitu dari BMKG Citeko dan CCROM-SEAP IPB. Penelitian ini dilakukan dengan menggunakan model WRF Chem untuk membantu analisis pola angin dengan periode waktu pemodelan selama 7 hari di bulan Agustus 2018. Hasil analisis menunjukkan konsentrasi NOx lebih tinggi di Baranangsiang. Konsentrasi ozon yang terukur menunjukkan nilai yang lebih tinggi di wilayah Citeko. Pada pukul 16.00 WIB hingga pukul 17.15 WIB selisih konsentrasi ozon di Citeko dan di Baranangsiang meningkat sehingga mencapai selisih nilai 48.67 μg/m3. Konsentrasi ozon yang tinggi di Citeko dipengaruhi oleh pola angin lokal yang dapat memengaruhi pergerakan pencemar. Hasil luaran model WRF Chem menunjukkan adanya pergerakan massa udara dari Baranangsiang menuju Citeko yang terjadi sekitar pukul 13.00 WIB hingga 15.00 WIB selama periode waktu pemodelan. Hasil ini mendukung bahwa konsentrasi yang tinggi di Citeko dapat dipengaruhi oleh adanya long range transport yang termasuk pada klasifikasi intermediate-range transport.
Keyword: long range transport, NOx, ozone, wind, WRF Chem

Judul: Surface Ozone Concentration and Relations to Ozone Precursor Concentration and Meteorological Factors Case Study: Bogor City.
Abstrak: Kualitas udara Kota Bogor dipengaruhi oleh aktivitas manusia. Hal ini menyebabkan peningkatan konsentrasi polutan di udara, diantaranya adalah O3 yang merupakan polutan sekunder. Penelitian ini bertujuan untuk menganalisis prekursor dan faktor meteorologi yang paling berpengaruh terhadap pembentukan O3 di atmosfer. Metode yang digunakan adalah analisis korelasi pearson. Periode waktu yang digunakan adalah curah hujan rendah dan curah hujan tinggi yang diwakili oleh bulan April 2022 dan Maret 2023. Prekursor ozon yang digunakan adalah NO, NO2, dan CO, sedangkan faktor meteorologi yang digunakan adalah suhu udara, kelembaban, kecepatan dan arah angin. Koefisien korelasi NO, NO2 dan CO pada curah hujan tinggi (r = -0,641; r = 0,122; r = -0,077) dan curah hujan rendah (r = -0,446; r = 0,167; r = -0,143). Koefisien korelasi suhu udara, kelembaban, dan kecepatan angin pada curah hujan tinggi (r = 0,631; r = -0,503; r = 0,283) dan curah hujan rendah (r = 0,599; r = -0,470; r = 0,223). NO merupakan prekursor O3 yang memiliki hubungan kuat pada curah hujan rendah (r = -0,446) dan curah hujan tinggi (r = -0,641). Suhu udara merupakan faktor meteorologi yang berhubungan kuat pada curah hujan rendah (r = 0,599) dan curah hujan tinggi (r = 0,631)., Bogor City's air quality is influenced by human activities. This causes an increase in the concentration of pollutants in the air, including O3 which is a secondary pollutant. This study aims to analyse the precursors and meteorological factors that have the most influence on the formation of O3 in the atmosphere. The method used was Pearson correlation analysis. The time period used is low rainfall and high rainfall represented by April 2022 and March 2023. The ozone precursors used are NO, NO2, and CO, while the meteorological factors used are air temperature, humidity, wind speed and direction. The correlation coefficients of NO, NO2 and CO at high rainfall (r = -0.641; r = 0.122; r = -0.077) and low rainfall (r = -0.446; r = 0.167; r = -0.143). The correlation coefficient of air temperature, humidity, and wind speed in high rainfall (r = 0.631; r = -0.503; r = 0.283) and low rainfall (r = 0.599; r = -0.470; r = 0.223). NO is a precursor of O3 which has a strong relation in low rainfall (r = -0.446) and high rainfall (r = -0.641). Air temperature is a meteorological factor that is strong relation with low rainfall (r = 0.599) and high rainfall (r = 0.631)., ,
Keyword: Atmosfer, Faktor Meteorologi, Ozon, Polutan, Prekursor

Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm