Update README.md

README.md

@@ -8,12 +8,12 @@
 This repository contains the source code for the [OpenAQ API](https://api.openaq.org), a publicly-accessible API that provides endpoints to query the real-time and historical air quality measurements on the OpenAQ platform.
 
 > [!NOTE]  
-> This repository is for setting up and deploying the OpenAQ API. If you just wish to access the public API to query data from the OpenAQ platform visit https://api.openaq.org or https://docs.openaq.org to learn more. 
+> This repository is for setting up and deploying the OpenAQ API. If you just wish to access the public API to query data from the OpenAQ platform, visit https://api.openaq.org or https://docs.openaq.org to learn more. 
 
 
 ### Platform Overview
 
-[openaq-fetch](https://github.com/openaq/openaq-fetch) and [openaq-fetch-lcs](https://github.com/openaq/openaq-fetch-lcs) take care of fetching new data and writing to [S3](https://openaq-fetches.s3.amazonaws.com/index.html). Lambda functions defined in [ingest/](openaq_api/openaq_api/ingest/) then load data into the database, defined in [openaq-db](https://github.com/openaq/openaq-db).
+[openaq-fetch](https://github.com/openaq/openaq-fetch) and [openaq-fetch-lcs](https://github.com/openaq/openaq-fetch-lcs) take care of fetching new data and writing to [S3](https://openaq-fetches.s3.amazonaws.com/index.html). Lambda functions defined in [ingest/](openaq_api/openaq_api/ingest/), then load data into the database, defined in [openaq-db](https://github.com/openaq/openaq-db).
 
 ## Getting started
 
@@ -25,13 +25,13 @@ Deployment is managed with Amazon Web Services (AWS) Cloud Development Kit (CDK)
 
 ## Local Development Environment
 
-In production the OpenAQ API runs on AWS Lambda with the help of the [mangum](https://mangum.io/) library. This allows the application to run in a serverless envionment and take advantage of async Python and FastAPI routing. Despite the serverless deployment, running the API locally as a standard FastAPI application is largely unchanged, making local development much easier. 
+In production, the OpenAQ API runs on AWS Lambda with the help of the [mangum](https://mangum.io/) library. This allows the application to run in a serverless environment and take advantage of async Python and FastAPI routing. Despite the serverless deployment, running the API locally as a standard FastAPI application is largely unchanged, making local development much easier. 
 
 ### Settings
-Settings can be loaded using `.env` files and multiple files can be kept and used. The easiest way to manage multiple environment files is to add an extension describing your environment. For example, if I wanted to keep a production, staging and local environment I would save them as `.env.production`, `.env.staging` and `.env.local` each with their own settings.
+Settings can be loaded using `.env` files, and multiple files can be kept and used. The easiest way to manage multiple environment files is to add an extension describing your environment. For example, if I wanted to keep a production, staging and local environment, I would save them as `.env.production`, `.env.staging` and `.env.local` each with their own settings.
 
 ### uvicorn
-The easiest way to run the API locally is to use uvicorn. Make sure that you have your settings (`.env`) file setup. Once that is done you can run the following from the `openaq_api/openaq_api` directory. Variables from the `.env` files can be overrode by setting them inline.
+The easiest way to run the API locally is to use uvicorn. Make sure that you have your settings (`.env`) file setup. Once that is done, you can run the following from the `openaq_api/openaq_api` directory. Variables from the `.env` files can be overrode by setting them inline.
 ```
 # Run using the default .env file
 uvicorn main:app --reload
@@ -82,7 +82,7 @@ Additional environmnet variables are required for a full deployment to the AWS C
 
 ## Rate limiting
 
-In the production environment rate limiting is handled in two places, AWS WAF and at the application level with [Starlette Middleware](https://www.starlette.io/middleware/). The application rate limiting is configurable via environment variables. The rate limiting middleware requires access to an instance of a [redis](https://redis.io/) cluster. For local development, [docker](https://www.docker.com/) can be a convenient method to set up a local redis cluster. With docker, use the following command:
+In the production environment, rate limiting is handled in two places, AWS WAF and at the application level with [Starlette Middleware](https://www.starlette.io/middleware/). The application rate limiting is configurable via environment variables. The rate limiting middleware requires access to an instance of a [redis](https://redis.io/) cluster. For local development, [docker](https://www.docker.com/) can be a convenient method to set up a local redis cluster. With docker, use the following command:
 
 ```sh
 docker run -e "IP=0.0.0.0" -p 7000-7005:7000-7005 grokzen/redis-cluster:7.0.7
@@ -97,7 +97,7 @@ Now a redis instance will be available at ``` http://localhost:7000 ```. Configu
 
 Rate limiting can be toggled off for local develop via the `RATE_LIMITING` environment variable. Other rate limiting values are:
 * `RATE_AMOUNT` - The number of requests allowed without a valid API key
-* `RATE_AMOUNT_KEY` - The number of requests allow with a valid API key
+* `RATE_AMOUNT_KEY` - The number of requests allowed with a valid API key
 * `RATE_TIME` - The number of minutes for the rate
 
 e.g. `RATE_AMOUNT=5` and `RATE_TIME=1` would allow 5 requests per 1 minute.
@@ -107,4 +107,4 @@ e.g. `RATE_AMOUNT=5` and `RATE_TIME=1` would allow 5 requests per 1 minute.
 
 
 ## Contributing
-There are a lot of ways to contribute to this project, more details can be found in the [contributing guide](CONTRIBUTING.md).
+There are many ways to contribute to this project; more details can be found in the [contributing guide](CONTRIBUTING.md).