Towards improved models for indoor air chemistry: A Monte Carlo simulation study

Kruza, Magdalena; Shaw, David R.; Shaw, Jacob; Carslaw, Nicola

doi:10.1016/j.atmosenv.2021.118625

Cited by 9 publications

(8 citation statements)

References 42 publications

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“…• mixing ratio of approximately 0.8 ppt 34,35 and 2 ppt for NO. 36 Lifetimes of siloxane-derived RO 2…”

Section: Atmospheric Implicationsmentioning

confidence: 99%

“…To understand siloxane-derived RO 2 • fate in a variety of atmospheric scenarios, we use data from the California Research at the Nexus of Air Quality (CalNex) campaign to represent an urban atmosphere and data from the Southern Oxidant and Aerosol Study (SOAS) to represent rural conditions. Urban conditions assumed midday mixing ratios of 10 ppt HO 2 • and 5 ppb NO. , For rural conditions, we assumed midday mixing ratios of 25 ppt HO 2 • and 50 ppt NO. , For indoor conditions, we assume an HO 2 • mixing ratio of approximately 0.8 ppt , and 2 ppt for NO . Lifetimes of siloxane-derived RO 2 • radicals based on these conditions are presented in Figure .…”

Section: Atmospheric Implicationsmentioning

confidence: 99%

See 1 more Smart Citation

Volatile Methyl Siloxane Atmospheric Oxidation Mechanism from a Theoretical Perspective─How is the Siloxanol Formed?

Alton,

Johnson,

Sharma

et al. 2023

J. Phys. Chem. A

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“…• mixing ratio of approximately 0.8 ppt 34,35 and 2 ppt for NO. 36 Lifetimes of siloxane-derived RO 2…”

Section: Atmospheric Implicationsmentioning

confidence: 99%

Section: Atmospheric Implicationsmentioning

confidence: 99%

Volatile Methyl Siloxane Atmospheric Oxidation Mechanism from a Theoretical Perspective─How is the Siloxanol Formed?

Alton,

Johnson,

Sharma

et al. 2023

J. Phys. Chem. A

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“…In INCHEM-Py only indoor species are predicted, and in some cases their concentrations will depend heavily on influx from and efflux to outdoors (Kruza et al, 2021). All indoor species are set to decay at a rate of λ r C i and to increase at a rate of λ r C i,out as in Eq.…”

Section: Outdoor Air Exchangementioning

confidence: 99%

INCHEM-Py v1.2: a community box model for indoor air chemistry

Shaw,

Carter,

Davies

et al. 2023

Geosci. Model Dev.

Self Cite

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Abstract. The Indoor CHEMical model in Python, INCHEM-Py, is an open-source and accessible box model for the simulation of the indoor atmosphere and is a refactor (rewrite of source code) and significant development of the INdoor Detailed Chemical Model (INDCM). INCHEM-Py creates and solves a system of coupled ordinary differential equations that include gas-phase chemistry, surface deposition, indoor–outdoor air change, indoor photolysis processes and gas-to-particle partitioning for three common terpenes. It is optimised for ease of installation and simple modification for inexperienced users, while also providing unfettered access to customise the physical and chemical processes for more advanced users. A detailed user manual is included with the model and updated with each version release. In this paper, INCHEM-Py v1.2 is introduced, and the modelled processes are described in detail, with benchmarking between simulated data and published experimental results presented, alongside discussion of the parameters and assumptions used. It is shown that INCHEM-Py achieves excellent agreement with measurements from an experimental campaign which investigate the effects of different surfaces on the concentrations of different indoor air pollutants. In addition, INCHEM-Py shows closer agreement to experimental data than INDCM. This is due to the increased functionality of INCHEM-Py to model additional processes, such as deposition-induced surface emissions. A comparative analysis with a similar zero-dimensional model, AtChem2, verifies the solution of the gas-phase chemistry. Published community use cases of INCHEM-Py are also presented to show the variety of applications for which this model is valuable to further our understanding of indoor air chemistry.

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“…Prediksi bisa dikatakan sebagai sebuah model yang sensitif terhadap jumlah parameter input yang kompleks dan sering digabungkan. Beberapa di antara parameter tersebut memiliki rentang nilai yang dapat diterima dari literatur dan oleh karena itu memilih nilai yang sesuai merupakan sebuah pekerjaan yang tidak mudah [7]. Simulasi Monte Carlo ini dibangun berdasarkan pada penggunaan angka-angka yang bersifat random untuk mengidentifikasi sebuah permasalahan [8].…”

Section: Pendahuluanunclassified

Monte Carlo Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo

Zaimy¹,

Defit

Nurcahyo

2021

jidt

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Stunting merupakan kondisi gagal tumbuh pada anak balita (bayi di bawah lima tahun) akibat dari kekurangan gizi kronis sehingga anak terlalu pendek untuk usianya. Menurut data yang ada, angka prevalensi stunting di Kabupaten Lima Puluh Kota tahun 2020 cukup tinggi yakni sebesar 8,28%. Hal ini menjadi perhatian pemerintah pusat dengan menetapkan Kabupaten Lima Puluh Kota sebagai salah satu Kabupaten/Kota Lokasi Fokus Intervensi Penurunan Stunting Terintegrasi Secara Nasional. Hasil penelitian ini bertujuan untuk membantu Pemerintah Kabupaten Lima Puluh Kota dalam perencanaan konvergensi program/intervensi sebagai salah satu upaya percepatan pencegahan stunting dan menurunkan persentase balita stunting di Kabupaten Lima Puluh Kota. Data penelitian ini menggunakan angka prevalensi stunting dari tahun 2018 sampai tahun 2020 yang berasal dari data jumlah balita dan jumlah balita stunting dari 22 puskesmas yang berada di Kabupaten Lima Puluh Kota. Selanjutnya data tersebut diolah menggunakan metode Monte Carlo untuk memprediksi tingkat prevalensi stunting tahun 2021. Berdasarkan pengujian yang dilakukan menggunakan metode Monte Carlo, didapatkan hasil tingkat prediksi stunting yang tertinggi berada pada puskesmas Pakan Rabaa dan puskesmas Suliki dengan angka prevalensi stunting sebesar 11,70%. Adapun tingkat akurasi yang diperoleh sebesar 93,73 %. Metode Monte Carlo cocok digunakan untuk prediksi angka prevalensi stunting di Kabupaten Lima Puluh Kota dilihat dari tingkat akurasinya yang cukup tinggi dari hasil pengolahan data.

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Towards improved models for indoor air chemistry: A Monte Carlo simulation study

Cited by 9 publications

References 42 publications

Volatile Methyl Siloxane Atmospheric Oxidation Mechanism from a Theoretical Perspective─How is the Siloxanol Formed?

Volatile Methyl Siloxane Atmospheric Oxidation Mechanism from a Theoretical Perspective─How is the Siloxanol Formed?

INCHEM-Py v1.2: a community box model for indoor air chemistry

Monte Carlo Prediksi Tingkat Prevalensi Stunting Kabupaten Lima Puluh Kota Menggunakan Metode Monte Carlo

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