PM2.5 Map by fusing Machine-learning and Kriging estimates

Just a brief update on our progress in making PM2.5 maps for the northeast. First we applied machine learning algorithms to estimate PM2.5 from remote sensing, ground station and meteorology data, then we fused Kriging results of the ground station data to obtain the final PM2.5 map. Inverse distance weighting on remote sensing has been applied to improve the coverage on remote sensing. The results were obtained using NY state data as we were funded by NY state agency. 

An interview with Dr. Suman Neupane

Congratulations to Dr. Suman Neupane (Scholar page) for successfully defending his PhD thesis from Physics Department (Florida International University). He is also the recipient of the best dissertation award (more here!). We gladly present an interview with him.

1.     Please tell us about yourself. (Nepal School, Masters experience and travel to PhD institutions. Did you teach? Also any links, personal websites, and a photo etc.)

I come from a middle class family of Chapagaon which lies in the outskirt of Kathmandu valley. After finishing high school from Lalitpur Madhyamik Vidyalaya, I joined Amrit Science College with the aim of studying physics to prepare for a teaching career at higher education. I finished Bachelors in science from Tri-Chandra college and Masters’ in Physics from the Central Department of Physics In Tribhuvan University. During the transitional periods from one level to another, I taught in different schools for a total time of about 30 months.  After my Masters degree, I taught physics for three years in Kathmandu and joined Florida International University in January 2008.

2.       Could you please describe your PhD research in plain English?

During my PhD, I was involved in the experimental study of carbon nanotubes (CNTs) and related materials. A graphene (Nobel prize, 2010) layer consists of carbon atoms arranged in a hexagonal arrays. A CNT can be visualized a multiple layer of graphene rolled into a tubular structure. As the name suggests, the diameter of CNTs can be as little as few nanometers (one-billionth of a meter) while the length could run to several hundred micrometers.  Due to their special structure, CNTs have strength greater than of steel and are several times lighter than aluminum. CNTs have potential in application of being used as electron source for displays, electrode in lithium ion battery, agent for drug delivery, composite materials for high-strength materials, etc. During my PhD, I was primarily focused in enhancing the electron emission properties of CNT arrays, studying the structural evolution of carbon nanotubes during lithiation and delithiation cycles in lithium ion batteries. I was also involved in the research of materials like ruthenium dioxide, titanium dioxide for energy storage applications.

3.       What are the social applications of your research/ short-term or long-term impact of your research to the society?

Carbon nanotube has potential for applications in various fields: Carbon nanotubes have been added to strengthen materials for sports equipment, body armor, vehicles, rockets, and building materials.  CNTs also find applications in solar cells for renewable source of energy. Using carbon nanotubes as the electrodes in lithium ion battery, capacitors provides more current and better electrical and mechanical stability than other leading materials. Carbon nanotube based devices can be used in efficient displays. In the long term, CNTs can are also expected to play a major role in biomedical applications.

4.       How was your graduate school experience?

My graduate school was a big learning experience. Coming from a background with a very little experimental skills, it takes a lot of time and effort to learn several skills for survival. Closely following post-docs and senior graudate students will help to get through ups and down of graduate school. One needs overall transformation, dexterity, reading and writing skills, computational skills  all needed to be acquired.

5.       Please share few useful tips that you wish you were told when you applied for PhD.

A student coming to graduate school should be prepared to around 5 years of hardship. It would take at 10 year for  a person to get a real job and settle down. So, the message to convey to a enthusiast is that THIS IS A NEW BEGINNING. While choosing the school, choose the school which has research going in right direction. Go to
(i) Google scholar and check where the professors are publishing recently.
(ii) Check the funding history of the professors
(iii) Check where the recent graduates are??
(iv) Check the RATE MY PROFESSOR, this might give a little idea about the attitude of the professor.

6.       Where do you want to be in the next 5 years? What are your hobbies, and spare time activities?

I have recently joined as a post doctoral associate continuing the research. It will be another challenging 5 years. Joining a research institute will be an ideal case scenario.
For the hobbies, I prefer playing and watching sports, traveling, watching games, following news from around the world.

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Interview with Dr. Chetan Dhital

In our effort to highlight Nepali physicists, we are inviting recent physics-PhD graduates. We congratulate Dr. Chetan Dhital for defending his PhD in Physics. Presenting a brief interview with him.

First of all, thank you Nabin jee for your time and effort.

Q. Please tell us about yourself. (Nepal School, Masters experience and travel to PhD institutions. Did you teach? Also any links, personal websites, and a photo etc.)

I was born in Bahundangi, a remote village in Jhapa, Nepal. I passed SLC from a government school near my village. When I passed SLC, my goal was to become a B.Sc. teacher so I joined I. Sc. in MMAM Campus Biratnagar where I finished both I. Sc. and B.Sc. After finishing B.Sc., I taught in a boarding high school for about 1 year. During that one year I changed my mind and went to Kathmandu to study M. Sc. in physics. I started to realize the beauty and importance of physics during those four years of my Master study. After finishing M.Sc, I taught undergraduate physics courses in Damak Multiple Campus Damak, Jhapa Nepal for about 3 years. In 2008, I got the opportunity to come to Boston College for pursuing Ph.D. in physics.

Q. Could you please describe your PhD research in plain English.  

My work during Ph.D is more about the fundamental physics which may not have direct immediate application. I worked mainly on two systems (1) Oxides of Iridium (2) Iron based superconductors. My work is focused in understanding different exotic electronic/magnetic phases in these materials by measuring electrical transport, magnetization and neutron scattering techniques.

Wait: why exotic?

In a hand waving argument, if one tries to confine charged particles in a small volume then charges experience mutual electric repulsion which blocks their movement resulting in an insulator so called Mott insulator. If the charges are allowed to stay inside a larger volume, they can avoid strong repulsion and may result in conducting states if the volume (band) is half filled. This is main theme of conventional band theory. In a solid the charged particles are electrons and the volume is the orbital occupied by electrons. If we believe the above picture and take the particular examples of Sr₃Ru₂O₇ and Sr₃Ir₂O₇, we should expect more metallic behavior in Sr₃Ir₂O₇ than that in Sr₃Ru₂O₇ because 5d orbitals of iridium are more extended than 4d orbitals of ruthenium. But the reality is opposite i.e Sr₃Ir₂O₇ is insulator and Sr₃Ru₂O₇ is a metal. In fact most of the oxides containing iridium in its 4⁺ valence state show such deviation from conventional wisdom. This is why they are exotic. Here the major player is spin-orbit interaction strength in iridates which is not just a perturbation term as in 3d compounds. Thus iridates (oxides of iridium) host many exotic quantum phases like spin liquid, quantum spin ice, Mott-insulators etc. We map out the electronic/magnetic phase behavior some typical doped and parent iridates. Regarding the second project, the key question in high temperature superconductivity is the mechanism responsible for superconductivity. In iron-based superconductors, the superconducting transition is always preceded by crystal and magnetic structural transition. My study is focused in understanding the structural and magnetic phase behavior of the electron doped superconducting system via neutron scattering.

Q. What are the social applications of your research/ short-term or long-term impact of your research to the society?

As I already mentioned, they may not have direct social impact. However, as we know transition metal oxides are also called functional materials, which have very good thermal and chemical stability allowing their use over a wide range of temperature and different chemical environments. In fact modern day electronics are based on transition metal oxide devices. The properties of these oxides are governed by the interplay of different competing energy scales. More players mean more ways of tuning the properties of these functional materials.
I think I do not have to say anything about the social/economic impact of high temperature superconductor (I wish we had room temperature superconductor) in this fast paced world where we are severely lacking our energy demand. But, to know the superconductivity better, one has to know ‘what was there at high temperature that becomes superconductor at low temperature’. My research is about “what was at high temperature”?

Q. How was your graduate school experience? (Specifically in terms of preparations towards your PhD, awards etc. Which skill(s) in particular you needed to sharpen, skills that you already had from previous institutions etc.)

In my view, the graduate study in USA is more student centered and practical. However, the courses we took in Master level especially the solid-state physics courses were very helpful. For my case, the graduate study period was satisfactory. I had the opportunity to perform several experiments in different national laboratories around the world. I think the productivity depends up on several factors such as your devotion and interest in the work, your relation with PhD advisor, your field of study etc.
In my case, there was a good combination of all these factors. I was awarded with GMAG student dissertation award from American Physical Society. This award is given every year for 2 or 3 graduate students working in magnetism who are going to graduate within September of that year. The student has to be a member of GMAG unit in APS and should be nominated by his advisor. There are some other awards that are also included in the following link.
http://www.aps.org/units/gmag/newsletters/upload/february14.pdfI also authored/coauthored about 16 peer reviewed journals which can be found in the following link:
http://scholar.google.com/citations?user=hEbr_o4AAAAJ&hl=enIf you are working in Neutron scattering then there is a website for Neutron scattering society of America which provides information about awards and conferences.http://neutronscattering.org/

Q. Please share few useful tips that you wish you were told when you applied for PhD.

To be honest, I was not fully aware of American style when I applied for graduate program. I used to write email to office secretary rather than professors or graduate advisor, which was a big mistake. Here, one can directly write to graduate advisor without any hesitation. Nowadays, the access to internet is easier than the time I applied, so one can easily find the departments that match with his research interest. Although our interest does not always work, however, I would suggest giving priority to those departments where your research interests match.  If you have two options, then money should not be the primary factor for decision-making. Furthermore, familiarize yourself with some common programs like matlab, origin, mathematica, igor etc before coming here. I think there should be a computational course at least in master level physics.

Q. Where do you want to be in the next 5 years? What are your hobbies, and spare time activities?

For the next step, I am joining as a postdoctoral research associate in Oak Ridge National Laboratory.  My next step is to give a shot for research faculty in suitable graduate schools (You miss 100% of the shots you don’t take,). If that doesn’t work, I want to stay in some suitable research and development department. However, I like to say “it is life”.
If you have kids then definition of spare time becomes vague. However, if I have time then I know how to watch basketball, soccer and cricket. I also enjoy watching comedy programs and movies and of course Nepal and world news.

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Presenting in Machine Learning Conference in NYAS today

Creating High-Resolution Climate Meteorological Forecasts by Application of Machine Learning Techniques

Nabin Malakar, PhD, Emmanuel Ekwedike, BS, Barry Gross, PhD, Jorge Gonzalez, PhD, and Charles Vorosmatry, PhD

The City College of New York, New York, New York, United States;

In order to study the effects of global climate change on a regional scale, the low resolution GCM forecast data needs to be intelligently adapted (downscaled) so that it can be injected into high resolution models such as terrestrial ecosystems. Our study region is the North East domain [{35N, 45N} x {-85W,-65W}]. In particular, we focus on High and Low temperature extremes within the Daymet data set, while the low resolution climatology (at 0.5 deg) MET data are obtained from the The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) climatology forecast database.  Although the injection of regional Meteorological Models such as Weather Research and Forecasting (WRF) can be attempted where the GCM conditions and the forecasted land surface properties are encoded into a future time slices, this approach is extremely computer intensive. We present a two-step mechanism by using low resolution meteorological data including both surface and column integrated parameters, and then by combining high resolution land surface classification parameters to improve on purely interpolative approaches by using machine learning techniques. 

Application of Machine-Learning for Estimation of PM2.5 by Data Fusion of Satellite Remote Sensing, Meteorological Factors, and Ground Station Data

Lina Cordero, MS, Nabin Malakar, PhD, Yonghua Wu, PhD,  Barry Gross, PhD,  Fred Moshary, PhD

Optical Remote Sensing Laboratory, CCNY, New York, New York, United States;

Particulate matter with dimension less than 2.5 micrometers (PM2.5) can have adverse health effects. These particles can enter into the blood streams via lungs, reach vital organs and cause serious damages by oxidative inflammations. We present our latest progress in obtaining correct estimates of PM2.5 on regional scale by using machine learning techniques. Specifically, we apply a neural network method for better describing the non-linear conditions surrounding the PM2.5-MODIS AOD while at the same time investigating dependencies on additional factors or seasonal changes.  In our local test, we find very good agreement of the neural network estimator when AOD, PBL, and seasonality are ingested (R~0.94 in summer). Next, we test our regional network and compare it with the GEOS-CHEM product. In particular, we find significant improvement of the NN approach with better correlation and less bias in comparison with GEOS-CHEM. We also show that further improvements are obtained if additional satellite information and land surface reflection, is included. Finally, comparisons with Community Multi-scale Air Quality Model (CMAQ) PM2.5 are also presented.

Using NN techniques to ingest Meteorological Weather Satellite data in support of Defense Satellite Observations

Crae Sosa, BS, Gary Bouton, MS, Sam Lightstone, MS, Nabin Malakar, PhD,  Barry Gross, PhD and Fred Moshary, PhD

The City College of New York, New York, New York, United States;

The need to observe thermal targets from space is crucial to monitoring both natural events and hostile threats. Satellites must choose between high spatial resolution with high sensitivity and multiple spectral channels. Defense satellites ultimately choose high sensitivity with a small number of spectral channels. This limitation makes atmospheric contamination due to water vapor a significant problem which can not be determined from the satellite itself. For this reason, we show how it is possible to ingest meteorological satellite data using NN to allow for the compensation of water absorption and re-emssion in near-real time

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